You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@mxnet.apache.org by aa...@apache.org on 2020/11/03 20:38:38 UTC
[incubator-mxnet-site] branch asf-site updated: Publish triggered
by CI
This is an automated email from the ASF dual-hosted git repository.
aaronmarkham pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new e36b65e Publish triggered by CI
e36b65e is described below
commit e36b65e4fb3e8d84bda481024c55ee22f73424d1
Author: mxnet-ci <mx...@amazon.com>
AuthorDate: Tue Nov 3 20:38:10 2020 +0000
Publish triggered by CI
---
api/clojure/docs/tutorials.html | 12 +-
api/perl/docs/tutorials.html | 12 +-
api/python/docs/_modules/index.html | 42 +-
api/python/docs/_modules/mxnet/autograd.html | 42 +-
api/python/docs/_modules/mxnet/callback.html | 42 +-
api/python/docs/_modules/mxnet/context.html | 42 +-
api/python/docs/_modules/mxnet/contrib/io.html | 42 +-
.../docs/_modules/mxnet/contrib/ndarray.html | 42 +-
.../mxnet/contrib/onnx/mx2onnx/export_model.html | 42 +-
.../mxnet/contrib/onnx/onnx2mx/import_model.html | 42 +-
.../contrib/onnx/onnx2mx/import_to_gluon.html | 42 +-
api/python/docs/_modules/mxnet/contrib/symbol.html | 42 +-
.../docs/_modules/mxnet/contrib/tensorboard.html | 42 +-
.../docs/_modules/mxnet/contrib/tensorrt.html | 42 +-
api/python/docs/_modules/mxnet/dlpack.html | 42 +-
api/python/docs/_modules/mxnet/engine.html | 42 +-
api/python/docs/_modules/mxnet/executor.html | 42 +-
api/python/docs/_modules/mxnet/gluon/block.html | 48 +-
.../gluon/contrib/estimator/batch_processor.html | 42 +-
.../mxnet/gluon/contrib/estimator/estimator.html | 42 +-
.../gluon/contrib/estimator/event_handler.html | 42 +-
.../docs/_modules/mxnet/gluon/data/dataloader.html | 42 +-
.../docs/_modules/mxnet/gluon/data/dataset.html | 42 +-
.../docs/_modules/mxnet/gluon/data/sampler.html | 42 +-
.../_modules/mxnet/gluon/data/vision/datasets.html | 42 +-
.../mxnet/gluon/data/vision/transforms.html | 42 +-
api/python/docs/_modules/mxnet/gluon/loss.html | 42 +-
api/python/docs/_modules/mxnet/gluon/metric.html | 42 +-
.../_modules/mxnet/gluon/model_zoo/vision.html | 42 +-
.../mxnet/gluon/model_zoo/vision/alexnet.html | 42 +-
.../mxnet/gluon/model_zoo/vision/densenet.html | 42 +-
.../mxnet/gluon/model_zoo/vision/inception.html | 42 +-
.../mxnet/gluon/model_zoo/vision/mobilenet.html | 42 +-
.../mxnet/gluon/model_zoo/vision/resnet.html | 42 +-
.../mxnet/gluon/model_zoo/vision/squeezenet.html | 42 +-
.../_modules/mxnet/gluon/model_zoo/vision/vgg.html | 42 +-
.../docs/_modules/mxnet/gluon/nn/activations.html | 42 +-
.../docs/_modules/mxnet/gluon/nn/basic_layers.html | 42 +-
.../docs/_modules/mxnet/gluon/nn/conv_layers.html | 42 +-
.../docs/_modules/mxnet/gluon/parameter.html | 51 +-
.../_modules/mxnet/gluon/rnn/conv_rnn_cell.html | 42 +-
.../docs/_modules/mxnet/gluon/rnn/rnn_cell.html | 42 +-
.../docs/_modules/mxnet/gluon/rnn/rnn_layer.html | 42 +-
api/python/docs/_modules/mxnet/gluon/trainer.html | 42 +-
api/python/docs/_modules/mxnet/gluon/utils.html | 42 +-
.../docs/_modules/mxnet/image/detection.html | 42 +-
api/python/docs/_modules/mxnet/image/image.html | 42 +-
api/python/docs/_modules/mxnet/initializer.html | 42 +-
api/python/docs/_modules/mxnet/io/io.html | 42 +-
api/python/docs/_modules/mxnet/lr_scheduler.html | 42 +-
.../docs/_modules/mxnet/ndarray/contrib.html | 42 +-
api/python/docs/_modules/mxnet/ndarray/image.html | 42 +-
api/python/docs/_modules/mxnet/ndarray/linalg.html | 42 +-
.../docs/_modules/mxnet/ndarray/ndarray.html | 42 +-
api/python/docs/_modules/mxnet/ndarray/random.html | 42 +-
api/python/docs/_modules/mxnet/ndarray/sparse.html | 42 +-
api/python/docs/_modules/mxnet/ndarray/utils.html | 42 +-
.../docs/_modules/mxnet/optimizer/adadelta.html | 42 +-
.../docs/_modules/mxnet/optimizer/adagrad.html | 42 +-
api/python/docs/_modules/mxnet/optimizer/adam.html | 42 +-
.../docs/_modules/mxnet/optimizer/adamax.html | 42 +-
.../docs/_modules/mxnet/optimizer/dcasgd.html | 42 +-
api/python/docs/_modules/mxnet/optimizer/ftml.html | 42 +-
api/python/docs/_modules/mxnet/optimizer/ftrl.html | 42 +-
api/python/docs/_modules/mxnet/optimizer/lamb.html | 42 +-
api/python/docs/_modules/mxnet/optimizer/lans.html | 42 +-
api/python/docs/_modules/mxnet/optimizer/lars.html | 42 +-
.../docs/_modules/mxnet/optimizer/nadam.html | 42 +-
api/python/docs/_modules/mxnet/optimizer/nag.html | 42 +-
.../docs/_modules/mxnet/optimizer/optimizer.html | 42 +-
.../docs/_modules/mxnet/optimizer/rmsprop.html | 42 +-
api/python/docs/_modules/mxnet/optimizer/sgd.html | 42 +-
api/python/docs/_modules/mxnet/optimizer/sgld.html | 42 +-
.../docs/_modules/mxnet/optimizer/signum.html | 42 +-
.../docs/_modules/mxnet/optimizer/updater.html | 42 +-
api/python/docs/_modules/mxnet/profiler.html | 42 +-
api/python/docs/_modules/mxnet/recordio.html | 42 +-
api/python/docs/_modules/mxnet/rtc.html | 42 +-
api/python/docs/_modules/mxnet/runtime.html | 42 +-
api/python/docs/_modules/mxnet/symbol/contrib.html | 42 +-
api/python/docs/_modules/mxnet/symbol/image.html | 42 +-
api/python/docs/_modules/mxnet/symbol/linalg.html | 42 +-
api/python/docs/_modules/mxnet/symbol/random.html | 42 +-
api/python/docs/_modules/mxnet/symbol/sparse.html | 42 +-
api/python/docs/_modules/mxnet/symbol/symbol.html | 42 +-
api/python/docs/_modules/mxnet/test_utils.html | 42 +-
api/python/docs/_modules/mxnet/util.html | 42 +-
api/python/docs/_modules/mxnet/visualization.html | 42 +-
api/python/docs/_modules/numbers.html | 42 +-
api/python/docs/_modules/symbol.html | 42 +-
.../crash-course/0-introduction.ipynb | 95 +
.../getting-started/crash-course/1-ndarray.ipynb | 357 ----
.../getting-started/crash-course/1-nparray.ipynb | 458 +++++
.../getting-started/crash-course/2-create-nn.ipynb | 549 ++++++
.../getting-started/crash-course/2-nn.ipynb | 319 ----
.../getting-started/crash-course/3-autograd.ipynb | 406 ++++-
.../crash-course/4-components.ipynb | 611 +++++++
.../getting-started/crash-course/4-train.ipynb | 403 -----
.../getting-started/crash-course/5-datasets.ipynb | 452 +++++
.../getting-started/crash-course/5-predict.ipynb | 368 ----
.../getting-started/crash-course/6-train-nn.ipynb | 644 +++++++
.../getting-started/crash-course/6-use_gpus.ipynb | 300 ----
.../getting-started/crash-course/7-use-gpus.ipynb | 412 +++++
.../getting-started/crash-course/index.rst | 22 +-
.../tutorials/performance/backend/amp.ipynb | 2 +-
api/python/docs/_static/pygments.css | 8 +-
api/python/docs/api/autograd/index.html | 42 +-
api/python/docs/api/context/index.html | 42 +-
api/python/docs/api/contrib/index.html | 42 +-
api/python/docs/api/contrib/io/index.html | 42 +-
api/python/docs/api/contrib/ndarray/index.html | 175 +-
api/python/docs/api/contrib/onnx/index.html | 42 +-
api/python/docs/api/contrib/symbol/index.html | 175 +-
api/python/docs/api/contrib/tensorboard/index.html | 42 +-
api/python/docs/api/contrib/tensorrt/index.html | 42 +-
api/python/docs/api/contrib/text/index.html | 42 +-
api/python/docs/api/engine/index.html | 42 +-
api/python/docs/api/executor/index.html | 42 +-
api/python/docs/api/gluon/block.html | 42 +-
api/python/docs/api/gluon/constant.html | 42 +-
api/python/docs/api/gluon/contrib/index.html | 42 +-
api/python/docs/api/gluon/data/index.html | 42 +-
.../docs/api/gluon/data/vision/datasets/index.html | 42 +-
api/python/docs/api/gluon/data/vision/index.html | 42 +-
.../api/gluon/data/vision/transforms/index.html | 42 +-
api/python/docs/api/gluon/hybrid_block.html | 42 +-
api/python/docs/api/gluon/index.html | 42 +-
api/python/docs/api/gluon/loss/index.html | 42 +-
api/python/docs/api/gluon/metric/index.html | 42 +-
api/python/docs/api/gluon/model_zoo/index.html | 42 +-
api/python/docs/api/gluon/nn/index.html | 42 +-
api/python/docs/api/gluon/parameter.html | 42 +-
api/python/docs/api/gluon/rnn/index.html | 42 +-
api/python/docs/api/gluon/symbol_block.html | 42 +-
api/python/docs/api/gluon/trainer.html | 42 +-
api/python/docs/api/gluon/utils/index.html | 42 +-
api/python/docs/api/index.html | 42 +-
api/python/docs/api/initializer/index.html | 42 +-
api/python/docs/api/kvstore/index.html | 42 +-
api/python/docs/api/kvstore_server/index.html | 42 +-
api/python/docs/api/legacy/callback/index.html | 42 +-
api/python/docs/api/legacy/image/index.html | 42 +-
api/python/docs/api/legacy/index.html | 42 +-
api/python/docs/api/legacy/io/index.html | 42 +-
.../docs/api/legacy/ndarray/contrib/index.html | 175 +-
.../docs/api/legacy/ndarray/image/index.html | 42 +-
api/python/docs/api/legacy/ndarray/index.html | 42 +-
.../docs/api/legacy/ndarray/linalg/index.html | 42 +-
api/python/docs/api/legacy/ndarray/ndarray.html | 46 +-
api/python/docs/api/legacy/ndarray/op/index.html | 46 +-
.../docs/api/legacy/ndarray/random/index.html | 42 +-
.../docs/api/legacy/ndarray/register/index.html | 42 +-
.../docs/api/legacy/ndarray/sparse/index.html | 42 +-
.../docs/api/legacy/ndarray/utils/index.html | 42 +-
api/python/docs/api/legacy/recordio/index.html | 42 +-
.../docs/api/legacy/symbol/contrib/index.html | 175 +-
api/python/docs/api/legacy/symbol/image/index.html | 42 +-
api/python/docs/api/legacy/symbol/index.html | 42 +-
.../docs/api/legacy/symbol/linalg/index.html | 42 +-
api/python/docs/api/legacy/symbol/op/index.html | 46 +-
.../docs/api/legacy/symbol/random/index.html | 42 +-
.../docs/api/legacy/symbol/register/index.html | 42 +-
.../docs/api/legacy/symbol/sparse/index.html | 42 +-
api/python/docs/api/legacy/symbol/symbol.html | 46 +-
.../docs/api/legacy/visualization/index.html | 42 +-
api/python/docs/api/lr_scheduler/index.html | 42 +-
api/python/docs/api/np/arrays.html | 42 +-
api/python/docs/api/np/arrays.indexing.html | 42 +-
api/python/docs/api/np/arrays.ndarray.html | 42 +-
.../docs/api/np/generated/mxnet.np.absolute.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.add.html | 42 +-
.../docs/api/np/generated/mxnet.np.amax.html | 42 +-
.../docs/api/np/generated/mxnet.np.amin.html | 42 +-
.../docs/api/np/generated/mxnet.np.append.html | 42 +-
.../docs/api/np/generated/mxnet.np.arange.html | 42 +-
.../docs/api/np/generated/mxnet.np.arccos.html | 42 +-
.../docs/api/np/generated/mxnet.np.arccosh.html | 42 +-
.../docs/api/np/generated/mxnet.np.arcsin.html | 42 +-
.../docs/api/np/generated/mxnet.np.arcsinh.html | 42 +-
.../docs/api/np/generated/mxnet.np.arctan.html | 42 +-
.../docs/api/np/generated/mxnet.np.arctan2.html | 42 +-
.../docs/api/np/generated/mxnet.np.arctanh.html | 42 +-
.../docs/api/np/generated/mxnet.np.argmax.html | 42 +-
.../docs/api/np/generated/mxnet.np.argmin.html | 42 +-
.../api/np/generated/mxnet.np.argpartition.html | 42 +-
.../docs/api/np/generated/mxnet.np.argsort.html | 42 +-
.../docs/api/np/generated/mxnet.np.argwhere.html | 42 +-
.../docs/api/np/generated/mxnet.np.around.html | 42 +-
.../docs/api/np/generated/mxnet.np.array.html | 42 +-
.../api/np/generated/mxnet.np.array_split.html | 42 +-
.../docs/api/np/generated/mxnet.np.atleast_1d.html | 42 +-
.../docs/api/np/generated/mxnet.np.atleast_2d.html | 42 +-
.../docs/api/np/generated/mxnet.np.atleast_3d.html | 42 +-
.../docs/api/np/generated/mxnet.np.average.html | 42 +-
.../docs/api/np/generated/mxnet.np.bincount.html | 42 +-
.../np/generated/mxnet.np.broadcast_arrays.html | 42 +-
.../api/np/generated/mxnet.np.broadcast_to.html | 42 +-
.../docs/api/np/generated/mxnet.np.cbrt.html | 42 +-
.../docs/api/np/generated/mxnet.np.ceil.html | 42 +-
.../docs/api/np/generated/mxnet.np.clip.html | 42 +-
.../api/np/generated/mxnet.np.column_stack.html | 42 +-
.../api/np/generated/mxnet.np.concatenate.html | 42 +-
.../docs/api/np/generated/mxnet.np.copy.html | 42 +-
.../docs/api/np/generated/mxnet.np.copysign.html | 42 +-
.../docs/api/np/generated/mxnet.np.corrcoef.html | 42 +-
.../docs/api/np/generated/mxnet.np.correlate.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.cos.html | 42 +-
.../docs/api/np/generated/mxnet.np.cosh.html | 42 +-
.../api/np/generated/mxnet.np.count_nonzero.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.cov.html | 42 +-
.../docs/api/np/generated/mxnet.np.cross.html | 42 +-
.../docs/api/np/generated/mxnet.np.cumprod.html | 42 +-
.../docs/api/np/generated/mxnet.np.cumsum.html | 42 +-
.../docs/api/np/generated/mxnet.np.deg2rad.html | 42 +-
.../docs/api/np/generated/mxnet.np.degrees.html | 42 +-
.../docs/api/np/generated/mxnet.np.delete.html | 42 +-
.../docs/api/np/generated/mxnet.np.diff.html | 42 +-
.../docs/api/np/generated/mxnet.np.digitize.html | 42 +-
.../docs/api/np/generated/mxnet.np.divide.html | 42 +-
.../docs/api/np/generated/mxnet.np.divmod.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.dot.html | 42 +-
.../docs/api/np/generated/mxnet.np.dsplit.html | 42 +-
.../docs/api/np/generated/mxnet.np.dstack.html | 42 +-
.../docs/api/np/generated/mxnet.np.ediff1d.html | 42 +-
.../docs/api/np/generated/mxnet.np.einsum.html | 42 +-
.../docs/api/np/generated/mxnet.np.empty.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.exp.html | 42 +-
.../api/np/generated/mxnet.np.expand_dims.html | 42 +-
.../docs/api/np/generated/mxnet.np.expm1.html | 42 +-
.../docs/api/np/generated/mxnet.np.extract.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.eye.html | 42 +-
.../docs/api/np/generated/mxnet.np.fabs.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.fix.html | 42 +-
.../api/np/generated/mxnet.np.flatnonzero.html | 42 +-
.../docs/api/np/generated/mxnet.np.flip.html | 42 +-
.../docs/api/np/generated/mxnet.np.fliplr.html | 42 +-
.../docs/api/np/generated/mxnet.np.flipud.html | 42 +-
.../api/np/generated/mxnet.np.float_power.html | 42 +-
.../docs/api/np/generated/mxnet.np.floor.html | 42 +-
.../docs/api/np/generated/mxnet.np.fmax.html | 42 +-
.../docs/api/np/generated/mxnet.np.fmin.html | 42 +-
.../docs/api/np/generated/mxnet.np.fmod.html | 42 +-
.../docs/api/np/generated/mxnet.np.frexp.html | 42 +-
.../docs/api/np/generated/mxnet.np.full.html | 42 +-
.../docs/api/np/generated/mxnet.np.genfromtxt.html | 42 +-
.../docs/api/np/generated/mxnet.np.heaviside.html | 42 +-
.../docs/api/np/generated/mxnet.np.histogram.html | 42 +-
.../api/np/generated/mxnet.np.histogram2d.html | 42 +-
.../np/generated/mxnet.np.histogram_bin_edges.html | 42 +-
.../api/np/generated/mxnet.np.histogramdd.html | 42 +-
.../docs/api/np/generated/mxnet.np.hsplit.html | 42 +-
.../docs/api/np/generated/mxnet.np.hstack.html | 42 +-
.../docs/api/np/generated/mxnet.np.hypot.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.i0.html | 42 +-
.../docs/api/np/generated/mxnet.np.identity.html | 42 +-
.../docs/api/np/generated/mxnet.np.inner.html | 42 +-
.../docs/api/np/generated/mxnet.np.insert.html | 42 +-
.../docs/api/np/generated/mxnet.np.interp.html | 42 +-
.../docs/api/np/generated/mxnet.np.kron.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.lcm.html | 42 +-
.../docs/api/np/generated/mxnet.np.ldexp.html | 42 +-
.../docs/api/np/generated/mxnet.np.lexsort.html | 42 +-
.../api/np/generated/mxnet.np.linalg.cholesky.html | 42 +-
.../api/np/generated/mxnet.np.linalg.cond.html | 42 +-
.../docs/api/np/generated/mxnet.np.linalg.det.html | 42 +-
.../docs/api/np/generated/mxnet.np.linalg.eig.html | 42 +-
.../api/np/generated/mxnet.np.linalg.eigh.html | 42 +-
.../api/np/generated/mxnet.np.linalg.eigvals.html | 42 +-
.../api/np/generated/mxnet.np.linalg.eigvalsh.html | 42 +-
.../docs/api/np/generated/mxnet.np.linalg.inv.html | 42 +-
.../api/np/generated/mxnet.np.linalg.lstsq.html | 42 +-
.../np/generated/mxnet.np.linalg.matrix_power.html | 42 +-
.../np/generated/mxnet.np.linalg.matrix_rank.html | 42 +-
.../np/generated/mxnet.np.linalg.multi_dot.html | 42 +-
.../api/np/generated/mxnet.np.linalg.norm.html | 42 +-
.../api/np/generated/mxnet.np.linalg.pinv.html | 42 +-
.../docs/api/np/generated/mxnet.np.linalg.qr.html | 42 +-
.../api/np/generated/mxnet.np.linalg.slogdet.html | 42 +-
.../api/np/generated/mxnet.np.linalg.solve.html | 42 +-
.../docs/api/np/generated/mxnet.np.linalg.svd.html | 42 +-
.../np/generated/mxnet.np.linalg.tensorinv.html | 42 +-
.../np/generated/mxnet.np.linalg.tensorsolve.html | 42 +-
.../docs/api/np/generated/mxnet.np.linspace.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.log.html | 42 +-
.../docs/api/np/generated/mxnet.np.log10.html | 42 +-
.../docs/api/np/generated/mxnet.np.log1p.html | 42 +-
.../docs/api/np/generated/mxnet.np.log2.html | 42 +-
.../docs/api/np/generated/mxnet.np.logspace.html | 42 +-
.../docs/api/np/generated/mxnet.np.matmul.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.max.html | 42 +-
.../docs/api/np/generated/mxnet.np.maximum.html | 42 +-
.../docs/api/np/generated/mxnet.np.mean.html | 42 +-
.../docs/api/np/generated/mxnet.np.median.html | 42 +-
.../docs/api/np/generated/mxnet.np.meshgrid.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.min.html | 42 +-
.../docs/api/np/generated/mxnet.np.minimum.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.mod.html | 42 +-
.../docs/api/np/generated/mxnet.np.modf.html | 42 +-
.../docs/api/np/generated/mxnet.np.moveaxis.html | 42 +-
.../docs/api/np/generated/mxnet.np.msort.html | 42 +-
.../docs/api/np/generated/mxnet.np.multiply.html | 42 +-
.../docs/api/np/generated/mxnet.np.nan_to_num.html | 42 +-
.../docs/api/np/generated/mxnet.np.nanargmax.html | 42 +-
.../docs/api/np/generated/mxnet.np.nanargmin.html | 42 +-
.../docs/api/np/generated/mxnet.np.nancumprod.html | 42 +-
.../docs/api/np/generated/mxnet.np.nancumsum.html | 42 +-
.../docs/api/np/generated/mxnet.np.nanmax.html | 42 +-
.../docs/api/np/generated/mxnet.np.nanmedian.html | 42 +-
.../docs/api/np/generated/mxnet.np.nanmin.html | 42 +-
.../api/np/generated/mxnet.np.nanpercentile.html | 42 +-
.../docs/api/np/generated/mxnet.np.nanprod.html | 42 +-
.../api/np/generated/mxnet.np.nanquantile.html | 42 +-
.../docs/api/np/generated/mxnet.np.nanstd.html | 42 +-
.../docs/api/np/generated/mxnet.np.nansum.html | 42 +-
.../docs/api/np/generated/mxnet.np.nanvar.html | 42 +-
.../docs/api/np/generated/mxnet.np.ndarray.T.html | 42 +-
.../api/np/generated/mxnet.np.ndarray.flatten.html | 42 +-
.../api/np/generated/mxnet.np.ndarray.sort.html | 42 +-
.../api/np/generated/mxnet.np.ndarray.tolist.html | 42 +-
.../docs/api/np/generated/mxnet.np.negative.html | 42 +-
.../docs/api/np/generated/mxnet.np.nonzero.html | 42 +-
.../docs/api/np/generated/mxnet.np.ones.html | 42 +-
.../docs/api/np/generated/mxnet.np.ones_like.html | 42 +-
.../docs/api/np/generated/mxnet.np.outer.html | 42 +-
.../docs/api/np/generated/mxnet.np.partition.html | 42 +-
.../docs/api/np/generated/mxnet.np.percentile.html | 42 +-
.../docs/api/np/generated/mxnet.np.positive.html | 42 +-
.../docs/api/np/generated/mxnet.np.power.html | 42 +-
.../docs/api/np/generated/mxnet.np.prod.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.ptp.html | 42 +-
.../docs/api/np/generated/mxnet.np.quantile.html | 42 +-
.../docs/api/np/generated/mxnet.np.rad2deg.html | 42 +-
.../docs/api/np/generated/mxnet.np.radians.html | 42 +-
.../docs/api/np/generated/mxnet.np.ravel.html | 42 +-
.../docs/api/np/generated/mxnet.np.reciprocal.html | 42 +-
.../docs/api/np/generated/mxnet.np.remainder.html | 42 +-
.../docs/api/np/generated/mxnet.np.repeat.html | 42 +-
.../docs/api/np/generated/mxnet.np.reshape.html | 42 +-
.../docs/api/np/generated/mxnet.np.resize.html | 42 +-
.../docs/api/np/generated/mxnet.np.rint.html | 42 +-
.../docs/api/np/generated/mxnet.np.roll.html | 42 +-
.../docs/api/np/generated/mxnet.np.rollaxis.html | 42 +-
.../docs/api/np/generated/mxnet.np.rot90.html | 42 +-
.../docs/api/np/generated/mxnet.np.round_.html | 42 +-
.../api/np/generated/mxnet.np.searchsorted.html | 42 +-
.../np/generated/mxnet.np.set_printoptions.html | 42 +-
.../docs/api/np/generated/mxnet.np.sign.html | 42 +-
.../docs/api/np/generated/mxnet.np.signbit.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.sin.html | 42 +-
.../docs/api/np/generated/mxnet.np.sinh.html | 42 +-
.../docs/api/np/generated/mxnet.np.sort.html | 42 +-
.../docs/api/np/generated/mxnet.np.spacing.html | 42 +-
.../docs/api/np/generated/mxnet.np.split.html | 42 +-
.../docs/api/np/generated/mxnet.np.sqrt.html | 42 +-
.../docs/api/np/generated/mxnet.np.square.html | 42 +-
.../docs/api/np/generated/mxnet.np.squeeze.html | 42 +-
.../docs/api/np/generated/mxnet.np.stack.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.std.html | 42 +-
.../docs/api/np/generated/mxnet.np.subtract.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.sum.html | 42 +-
.../docs/api/np/generated/mxnet.np.swapaxes.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.tan.html | 42 +-
.../docs/api/np/generated/mxnet.np.tanh.html | 42 +-
.../docs/api/np/generated/mxnet.np.tensordot.html | 42 +-
.../docs/api/np/generated/mxnet.np.tile.html | 42 +-
.../docs/api/np/generated/mxnet.np.trace.html | 42 +-
.../docs/api/np/generated/mxnet.np.transpose.html | 42 +-
.../docs/api/np/generated/mxnet.np.trapz.html | 42 +-
.../docs/api/np/generated/mxnet.np.tril.html | 42 +-
.../docs/api/np/generated/mxnet.np.trim_zeros.html | 42 +-
.../api/np/generated/mxnet.np.true_divide.html | 42 +-
.../docs/api/np/generated/mxnet.np.trunc.html | 42 +-
.../docs/api/np/generated/mxnet.np.unique.html | 42 +-
.../docs/api/np/generated/mxnet.np.unwrap.html | 42 +-
api/python/docs/api/np/generated/mxnet.np.var.html | 42 +-
.../docs/api/np/generated/mxnet.np.vdot.html | 42 +-
.../docs/api/np/generated/mxnet.np.vsplit.html | 42 +-
.../docs/api/np/generated/mxnet.np.vstack.html | 42 +-
.../docs/api/np/generated/mxnet.np.where.html | 42 +-
.../docs/api/np/generated/mxnet.np.zeros.html | 42 +-
.../docs/api/np/generated/mxnet.np.zeros_like.html | 42 +-
api/python/docs/api/np/index.html | 42 +-
.../np/random/generated/mxnet.np.random.beta.html | 42 +-
.../generated/mxnet.np.random.chisquare.html | 42 +-
.../random/generated/mxnet.np.random.choice.html | 42 +-
.../generated/mxnet.np.random.exponential.html | 42 +-
.../api/np/random/generated/mxnet.np.random.f.html | 42 +-
.../np/random/generated/mxnet.np.random.gamma.html | 42 +-
.../random/generated/mxnet.np.random.gumbel.html | 42 +-
.../random/generated/mxnet.np.random.laplace.html | 42 +-
.../random/generated/mxnet.np.random.logistic.html | 42 +-
.../generated/mxnet.np.random.lognormal.html | 42 +-
.../generated/mxnet.np.random.multinomial.html | 42 +-
.../mxnet.np.random.multivariate_normal.html | 42 +-
.../random/generated/mxnet.np.random.normal.html | 42 +-
.../random/generated/mxnet.np.random.pareto.html | 42 +-
.../np/random/generated/mxnet.np.random.power.html | 42 +-
.../np/random/generated/mxnet.np.random.rand.html | 42 +-
.../random/generated/mxnet.np.random.randint.html | 42 +-
.../random/generated/mxnet.np.random.rayleigh.html | 42 +-
.../random/generated/mxnet.np.random.shuffle.html | 42 +-
.../random/generated/mxnet.np.random.uniform.html | 42 +-
.../random/generated/mxnet.np.random.weibull.html | 42 +-
api/python/docs/api/np/random/index.html | 42 +-
.../docs/api/np/routines.array-creation.html | 42 +-
.../docs/api/np/routines.array-manipulation.html | 42 +-
api/python/docs/api/np/routines.html | 42 +-
api/python/docs/api/np/routines.io.html | 42 +-
api/python/docs/api/np/routines.linalg.html | 42 +-
api/python/docs/api/np/routines.math.html | 42 +-
api/python/docs/api/np/routines.sort.html | 42 +-
api/python/docs/api/np/routines.statistics.html | 42 +-
.../api/npx/generated/mxnet.npx.activation.html | 42 +-
.../api/npx/generated/mxnet.npx.batch_dot.html | 42 +-
.../api/npx/generated/mxnet.npx.batch_flatten.html | 42 +-
.../api/npx/generated/mxnet.npx.batch_norm.html | 42 +-
.../api/npx/generated/mxnet.npx.convolution.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.cpu.html | 42 +-
.../api/npx/generated/mxnet.npx.cpu_pinned.html | 42 +-
.../npx/generated/mxnet.npx.current_context.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.dropout.html | 42 +-
.../api/npx/generated/mxnet.npx.embedding.html | 42 +-
.../npx/generated/mxnet.npx.fully_connected.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.gamma.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.gpu.html | 42 +-
.../npx/generated/mxnet.npx.gpu_memory_info.html | 42 +-
.../api/npx/generated/mxnet.npx.layer_norm.html | 42 +-
.../api/npx/generated/mxnet.npx.leaky_relu.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.load.html | 42 +-
.../generated/mxnet.npx.multibox_detection.html | 42 +-
.../npx/generated/mxnet.npx.multibox_prior.html | 42 +-
.../npx/generated/mxnet.npx.multibox_target.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.num_gpus.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.one_hot.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.pick.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.pooling.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.reset_np.html | 42 +-
.../api/npx/generated/mxnet.npx.reshape_like.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.rnn.html | 42 +-
.../api/npx/generated/mxnet.npx.roi_pooling.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.save.html | 42 +-
.../api/npx/generated/mxnet.npx.sequence_mask.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.set_np.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.sigmoid.html | 42 +-
.../api/npx/generated/mxnet.npx.smooth_l1.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.softmax.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.topk.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.waitall.html | 42 +-
api/python/docs/api/npx/index.html | 42 +-
api/python/docs/api/optimizer/index.html | 42 +-
api/python/docs/api/profiler/index.html | 42 +-
api/python/docs/api/rtc/index.html | 42 +-
.../runtime/generated/mxnet.runtime.Feature.html | 42 +-
.../runtime/generated/mxnet.runtime.Features.html | 42 +-
.../generated/mxnet.runtime.feature_list.html | 42 +-
api/python/docs/api/runtime/index.html | 42 +-
api/python/docs/api/test_utils/index.html | 42 +-
api/python/docs/api/util/index.html | 42 +-
api/python/docs/genindex.html | 76 +-
api/python/docs/index.html | 42 +-
api/python/docs/objects.inv | Bin 89363 -> 90871 bytes
api/python/docs/py-modindex.html | 42 +-
api/python/docs/search.html | 42 +-
api/python/docs/searchindex.js | 2 +-
api/python/docs/tutorials/deploy/export/index.html | 42 +-
.../docs/tutorials/deploy/export/index.html.bak | 42 +-
api/python/docs/tutorials/deploy/export/onnx.html | 42 +-
.../docs/tutorials/deploy/export/onnx.html.bak | 42 +-
api/python/docs/tutorials/deploy/index.html | 42 +-
api/python/docs/tutorials/deploy/index.html.bak | 42 +-
.../docs/tutorials/deploy/inference/cpp.html | 42 +-
.../docs/tutorials/deploy/inference/cpp.html.bak | 42 +-
.../inference/image_classification_jetson.html | 42 +-
.../inference/image_classification_jetson.html.bak | 42 +-
.../docs/tutorials/deploy/inference/index.html | 42 +-
.../docs/tutorials/deploy/inference/index.html.bak | 42 +-
.../docs/tutorials/deploy/run-on-aws/cloud.html | 42 +-
.../tutorials/deploy/run-on-aws/cloud.html.bak | 42 +-
.../docs/tutorials/deploy/run-on-aws/index.html | 42 +-
.../tutorials/deploy/run-on-aws/index.html.bak | 42 +-
.../docs/tutorials/deploy/run-on-aws/use_ec2.html | 42 +-
.../tutorials/deploy/run-on-aws/use_ec2.html.bak | 42 +-
.../tutorials/deploy/run-on-aws/use_sagemaker.html | 42 +-
.../deploy/run-on-aws/use_sagemaker.html.bak | 42 +-
api/python/docs/tutorials/extend/customop.html | 42 +-
api/python/docs/tutorials/extend/customop.html.bak | 42 +-
api/python/docs/tutorials/extend/index.html | 42 +-
api/python/docs/tutorials/extend/index.html.bak | 42 +-
.../crash-course/0-introduction.html | 159 +-
.../crash-course/0-introduction.html.bak | 159 +-
.../getting-started/crash-course/1-nparray.html | 405 +----
.../{1-ndarray.html.bak => 1-nparray.html.bak} | 405 +----
.../crash-course/{2-nn.html => 2-create-nn.html} | 830 +++++----
.../{2-nn.html.bak => 2-create-nn.html.bak} | 830 +++++----
.../getting-started/crash-course/3-autograd.html | 443 ++---
.../crash-course/3-autograd.html.bak | 443 ++---
.../{4-train.html => 4-components.html} | 636 +++----
.../crash-course/4-components.html.bak | 636 +++----
.../crash-course/{4-train.html => 5-datasets.html} | 606 +++----
.../{4-train.html => 5-datasets.html.bak} | 606 +++----
.../getting-started/crash-course/5-predict.html | 1813 --------------------
.../crash-course/5-predict.html.bak | 1813 --------------------
.../crash-course/{4-train.html => 6-train-nn.html} | 512 ++++--
.../{4-train.html => 6-train-nn.html.bak} | 512 ++++--
.../getting-started/crash-course/7-use-gpus.html | 269 ++-
.../crash-course/7-use-gpus.html.bak | 269 ++-
.../getting-started/crash-course/index.html | 79 +-
.../getting-started/crash-course/index.html.bak | 79 +-
.../gluon_from_experiment_to_deployment.html | 42 +-
.../gluon_from_experiment_to_deployment.html.bak | 42 +-
.../docs/tutorials/getting-started/index.html | 46 +-
.../docs/tutorials/getting-started/index.html.bak | 46 +-
.../logistic_regression_explained.html | 42 +-
.../logistic_regression_explained.html.bak | 42 +-
.../tutorials/getting-started/to-mxnet/index.html | 48 +-
.../getting-started/to-mxnet/index.html.bak | 48 +-
.../getting-started/to-mxnet/pytorch.html | 42 +-
.../getting-started/to-mxnet/pytorch.html.bak | 42 +-
api/python/docs/tutorials/index.html | 42 +-
api/python/docs/tutorials/index.html.bak | 42 +-
.../docs/tutorials/packages/autograd/index.html | 42 +-
.../tutorials/packages/autograd/index.html.bak | 42 +-
.../gluon/blocks/activations/activations.html | 42 +-
.../gluon/blocks/activations/activations.html.bak | 42 +-
.../packages/gluon/blocks/custom-layer.html | 42 +-
.../packages/gluon/blocks/custom-layer.html.bak | 42 +-
.../gluon/blocks/custom_layer_beginners.html | 42 +-
.../gluon/blocks/custom_layer_beginners.html.bak | 42 +-
.../tutorials/packages/gluon/blocks/hybridize.html | 42 +-
.../packages/gluon/blocks/hybridize.html.bak | 42 +-
.../tutorials/packages/gluon/blocks/index.html | 42 +-
.../tutorials/packages/gluon/blocks/index.html.bak | 42 +-
.../docs/tutorials/packages/gluon/blocks/init.html | 42 +-
.../tutorials/packages/gluon/blocks/init.html.bak | 42 +-
.../tutorials/packages/gluon/blocks/naming.html | 42 +-
.../packages/gluon/blocks/naming.html.bak | 42 +-
.../docs/tutorials/packages/gluon/blocks/nn.html | 42 +-
.../tutorials/packages/gluon/blocks/nn.html.bak | 42 +-
.../packages/gluon/blocks/parameters.html | 42 +-
.../packages/gluon/blocks/parameters.html.bak | 42 +-
.../packages/gluon/blocks/save_load_params.html | 42 +-
.../gluon/blocks/save_load_params.html.bak | 42 +-
.../packages/gluon/data/data_augmentation.html | 42 +-
.../packages/gluon/data/data_augmentation.html.bak | 42 +-
.../tutorials/packages/gluon/data/datasets.html | 42 +-
.../packages/gluon/data/datasets.html.bak | 42 +-
.../docs/tutorials/packages/gluon/data/index.html | 42 +-
.../tutorials/packages/gluon/data/index.html.bak | 42 +-
.../docs/tutorials/packages/gluon/image/index.html | 42 +-
.../tutorials/packages/gluon/image/index.html.bak | 42 +-
.../tutorials/packages/gluon/image/info_gan.html | 42 +-
.../packages/gluon/image/info_gan.html.bak | 42 +-
.../docs/tutorials/packages/gluon/image/mnist.html | 42 +-
.../tutorials/packages/gluon/image/mnist.html.bak | 42 +-
.../docs/tutorials/packages/gluon/index.html | 42 +-
.../docs/tutorials/packages/gluon/index.html.bak | 42 +-
.../tutorials/packages/gluon/loss/custom-loss.html | 42 +-
.../packages/gluon/loss/custom-loss.html.bak | 42 +-
.../docs/tutorials/packages/gluon/loss/index.html | 42 +-
.../tutorials/packages/gluon/loss/index.html.bak | 42 +-
.../packages/gluon/loss/kl_divergence.html | 42 +-
.../packages/gluon/loss/kl_divergence.html.bak | 42 +-
.../docs/tutorials/packages/gluon/loss/loss.html | 42 +-
.../tutorials/packages/gluon/loss/loss.html.bak | 42 +-
.../docs/tutorials/packages/gluon/text/gnmt.html | 42 +-
.../tutorials/packages/gluon/text/gnmt.html.bak | 42 +-
.../docs/tutorials/packages/gluon/text/index.html | 42 +-
.../tutorials/packages/gluon/text/index.html.bak | 42 +-
.../tutorials/packages/gluon/text/transformer.html | 42 +-
.../packages/gluon/text/transformer.html.bak | 42 +-
.../packages/gluon/training/fit_api_tutorial.html | 42 +-
.../gluon/training/fit_api_tutorial.html.bak | 42 +-
.../tutorials/packages/gluon/training/index.html | 42 +-
.../packages/gluon/training/index.html.bak | 42 +-
.../gluon/training/learning_rates/index.html | 42 +-
.../gluon/training/learning_rates/index.html.bak | 42 +-
.../learning_rates/learning_rate_finder.html | 42 +-
.../learning_rates/learning_rate_finder.html.bak | 42 +-
.../learning_rates/learning_rate_schedules.html | 42 +-
.../learning_rate_schedules.html.bak | 42 +-
.../learning_rate_schedules_advanced.html | 42 +-
.../learning_rate_schedules_advanced.html.bak | 42 +-
.../gluon/training/normalization/index.html | 42 +-
.../gluon/training/normalization/index.html.bak | 42 +-
.../tutorials/packages/gluon/training/trainer.html | 42 +-
.../packages/gluon/training/trainer.html.bak | 42 +-
api/python/docs/tutorials/packages/index.html | 42 +-
api/python/docs/tutorials/packages/index.html.bak | 42 +-
.../docs/tutorials/packages/kvstore/index.html | 42 +-
.../docs/tutorials/packages/kvstore/index.html.bak | 42 +-
.../docs/tutorials/packages/kvstore/kvstore.html | 42 +-
.../tutorials/packages/kvstore/kvstore.html.bak | 42 +-
.../docs/tutorials/packages/legacy/index.html | 42 +-
.../docs/tutorials/packages/legacy/index.html.bak | 42 +-
.../packages/legacy/ndarray/01-ndarray-intro.html | 42 +-
.../legacy/ndarray/01-ndarray-intro.html.bak | 42 +-
.../legacy/ndarray/02-ndarray-operations.html | 42 +-
.../legacy/ndarray/02-ndarray-operations.html.bak | 42 +-
.../legacy/ndarray/03-ndarray-contexts.html | 42 +-
.../legacy/ndarray/03-ndarray-contexts.html.bak | 42 +-
.../legacy/ndarray/gotchas_numpy_in_mxnet.html | 42 +-
.../legacy/ndarray/gotchas_numpy_in_mxnet.html.bak | 42 +-
.../tutorials/packages/legacy/ndarray/index.html | 42 +-
.../packages/legacy/ndarray/index.html.bak | 42 +-
.../packages/legacy/ndarray/sparse/csr.html | 42 +-
.../packages/legacy/ndarray/sparse/csr.html.bak | 42 +-
.../packages/legacy/ndarray/sparse/index.html | 42 +-
.../packages/legacy/ndarray/sparse/index.html.bak | 42 +-
.../packages/legacy/ndarray/sparse/row_sparse.html | 42 +-
.../legacy/ndarray/sparse/row_sparse.html.bak | 42 +-
.../legacy/ndarray/sparse/train_gluon.html | 42 +-
.../legacy/ndarray/sparse/train_gluon.html.bak | 42 +-
.../docs/tutorials/packages/np/cheat-sheet.html | 42 +-
.../tutorials/packages/np/cheat-sheet.html.bak | 42 +-
api/python/docs/tutorials/packages/np/index.html | 42 +-
.../docs/tutorials/packages/np/index.html.bak | 42 +-
.../docs/tutorials/packages/np/np-vs-numpy.html | 42 +-
.../tutorials/packages/np/np-vs-numpy.html.bak | 42 +-
.../tutorials/packages/onnx/fine_tuning_gluon.html | 42 +-
.../packages/onnx/fine_tuning_gluon.html.bak | 42 +-
api/python/docs/tutorials/packages/onnx/index.html | 42 +-
.../docs/tutorials/packages/onnx/index.html.bak | 42 +-
.../packages/onnx/inference_on_onnx_model.html | 42 +-
.../packages/onnx/inference_on_onnx_model.html.bak | 42 +-
.../docs/tutorials/packages/optimizer/index.html | 42 +-
.../tutorials/packages/optimizer/index.html.bak | 42 +-
api/python/docs/tutorials/packages/viz/index.html | 42 +-
.../docs/tutorials/packages/viz/index.html.bak | 42 +-
.../docs/tutorials/performance/backend/amp.html | 44 +-
.../tutorials/performance/backend/amp.html.bak | 44 +-
.../docs/tutorials/performance/backend/index.html | 42 +-
.../tutorials/performance/backend/index.html.bak | 42 +-
.../performance/backend/mkldnn/index.html | 42 +-
.../performance/backend/mkldnn/index.html.bak | 42 +-
.../performance/backend/mkldnn/mkldnn_readme.html | 42 +-
.../backend/mkldnn/mkldnn_readme.html.bak | 42 +-
.../tutorials/performance/backend/profiler.html | 42 +-
.../performance/backend/profiler.html.bak | 42 +-
.../docs/tutorials/performance/backend/tvm.html | 42 +-
.../tutorials/performance/backend/tvm.html.bak | 42 +-
.../tutorials/performance/compression/index.html | 42 +-
.../performance/compression/index.html.bak | 42 +-
.../tutorials/performance/compression/int8.html | 42 +-
.../performance/compression/int8.html.bak | 42 +-
api/python/docs/tutorials/performance/index.html | 42 +-
.../docs/tutorials/performance/index.html.bak | 42 +-
date.txt | 1 -
feed.xml | 2 +-
versions/master/api/clojure/docs/tutorials.html | 12 +-
versions/master/api/perl/docs/tutorials.html | 12 +-
.../master/api/python/docs/_modules/index.html | 42 +-
.../api/python/docs/_modules/mxnet/autograd.html | 42 +-
.../api/python/docs/_modules/mxnet/callback.html | 42 +-
.../api/python/docs/_modules/mxnet/context.html | 42 +-
.../api/python/docs/_modules/mxnet/contrib/io.html | 42 +-
.../docs/_modules/mxnet/contrib/ndarray.html | 42 +-
.../mxnet/contrib/onnx/mx2onnx/export_model.html | 42 +-
.../mxnet/contrib/onnx/onnx2mx/import_model.html | 42 +-
.../contrib/onnx/onnx2mx/import_to_gluon.html | 42 +-
.../python/docs/_modules/mxnet/contrib/symbol.html | 42 +-
.../docs/_modules/mxnet/contrib/tensorboard.html | 42 +-
.../docs/_modules/mxnet/contrib/tensorrt.html | 42 +-
.../api/python/docs/_modules/mxnet/dlpack.html | 42 +-
.../api/python/docs/_modules/mxnet/engine.html | 42 +-
.../api/python/docs/_modules/mxnet/executor.html | 42 +-
.../python/docs/_modules/mxnet/gluon/block.html | 48 +-
.../gluon/contrib/estimator/batch_processor.html | 42 +-
.../mxnet/gluon/contrib/estimator/estimator.html | 42 +-
.../gluon/contrib/estimator/event_handler.html | 42 +-
.../docs/_modules/mxnet/gluon/data/dataloader.html | 42 +-
.../docs/_modules/mxnet/gluon/data/dataset.html | 42 +-
.../docs/_modules/mxnet/gluon/data/sampler.html | 42 +-
.../_modules/mxnet/gluon/data/vision/datasets.html | 42 +-
.../mxnet/gluon/data/vision/transforms.html | 42 +-
.../api/python/docs/_modules/mxnet/gluon/loss.html | 42 +-
.../python/docs/_modules/mxnet/gluon/metric.html | 42 +-
.../_modules/mxnet/gluon/model_zoo/vision.html | 42 +-
.../mxnet/gluon/model_zoo/vision/alexnet.html | 42 +-
.../mxnet/gluon/model_zoo/vision/densenet.html | 42 +-
.../mxnet/gluon/model_zoo/vision/inception.html | 42 +-
.../mxnet/gluon/model_zoo/vision/mobilenet.html | 42 +-
.../mxnet/gluon/model_zoo/vision/resnet.html | 42 +-
.../mxnet/gluon/model_zoo/vision/squeezenet.html | 42 +-
.../_modules/mxnet/gluon/model_zoo/vision/vgg.html | 42 +-
.../docs/_modules/mxnet/gluon/nn/activations.html | 42 +-
.../docs/_modules/mxnet/gluon/nn/basic_layers.html | 42 +-
.../docs/_modules/mxnet/gluon/nn/conv_layers.html | 42 +-
.../docs/_modules/mxnet/gluon/parameter.html | 51 +-
.../_modules/mxnet/gluon/rnn/conv_rnn_cell.html | 42 +-
.../docs/_modules/mxnet/gluon/rnn/rnn_cell.html | 42 +-
.../docs/_modules/mxnet/gluon/rnn/rnn_layer.html | 42 +-
.../python/docs/_modules/mxnet/gluon/trainer.html | 42 +-
.../python/docs/_modules/mxnet/gluon/utils.html | 42 +-
.../docs/_modules/mxnet/image/detection.html | 42 +-
.../python/docs/_modules/mxnet/image/image.html | 42 +-
.../python/docs/_modules/mxnet/initializer.html | 42 +-
.../api/python/docs/_modules/mxnet/io/io.html | 42 +-
.../python/docs/_modules/mxnet/lr_scheduler.html | 42 +-
.../docs/_modules/mxnet/ndarray/contrib.html | 42 +-
.../python/docs/_modules/mxnet/ndarray/image.html | 42 +-
.../python/docs/_modules/mxnet/ndarray/linalg.html | 42 +-
.../docs/_modules/mxnet/ndarray/ndarray.html | 42 +-
.../python/docs/_modules/mxnet/ndarray/random.html | 42 +-
.../python/docs/_modules/mxnet/ndarray/sparse.html | 42 +-
.../python/docs/_modules/mxnet/ndarray/utils.html | 42 +-
.../docs/_modules/mxnet/optimizer/adadelta.html | 42 +-
.../docs/_modules/mxnet/optimizer/adagrad.html | 42 +-
.../python/docs/_modules/mxnet/optimizer/adam.html | 42 +-
.../docs/_modules/mxnet/optimizer/adamax.html | 42 +-
.../docs/_modules/mxnet/optimizer/dcasgd.html | 42 +-
.../python/docs/_modules/mxnet/optimizer/ftml.html | 42 +-
.../python/docs/_modules/mxnet/optimizer/ftrl.html | 42 +-
.../python/docs/_modules/mxnet/optimizer/lamb.html | 42 +-
.../python/docs/_modules/mxnet/optimizer/lans.html | 42 +-
.../python/docs/_modules/mxnet/optimizer/lars.html | 42 +-
.../docs/_modules/mxnet/optimizer/nadam.html | 42 +-
.../python/docs/_modules/mxnet/optimizer/nag.html | 42 +-
.../docs/_modules/mxnet/optimizer/optimizer.html | 42 +-
.../docs/_modules/mxnet/optimizer/rmsprop.html | 42 +-
.../python/docs/_modules/mxnet/optimizer/sgd.html | 42 +-
.../python/docs/_modules/mxnet/optimizer/sgld.html | 42 +-
.../docs/_modules/mxnet/optimizer/signum.html | 42 +-
.../docs/_modules/mxnet/optimizer/updater.html | 42 +-
.../api/python/docs/_modules/mxnet/profiler.html | 42 +-
.../api/python/docs/_modules/mxnet/recordio.html | 42 +-
.../master/api/python/docs/_modules/mxnet/rtc.html | 42 +-
.../api/python/docs/_modules/mxnet/runtime.html | 42 +-
.../python/docs/_modules/mxnet/symbol/contrib.html | 42 +-
.../python/docs/_modules/mxnet/symbol/image.html | 42 +-
.../python/docs/_modules/mxnet/symbol/linalg.html | 42 +-
.../python/docs/_modules/mxnet/symbol/random.html | 42 +-
.../python/docs/_modules/mxnet/symbol/sparse.html | 42 +-
.../python/docs/_modules/mxnet/symbol/symbol.html | 42 +-
.../api/python/docs/_modules/mxnet/test_utils.html | 42 +-
.../api/python/docs/_modules/mxnet/util.html | 42 +-
.../python/docs/_modules/mxnet/visualization.html | 42 +-
.../master/api/python/docs/_modules/numbers.html | 42 +-
.../master/api/python/docs/_modules/symbol.html | 42 +-
.../crash-course/0-introduction.ipynb | 95 +
.../getting-started/crash-course/1-ndarray.ipynb | 357 ----
.../getting-started/crash-course/1-nparray.ipynb | 458 +++++
.../getting-started/crash-course/2-create-nn.ipynb | 549 ++++++
.../getting-started/crash-course/2-nn.ipynb | 319 ----
.../getting-started/crash-course/3-autograd.ipynb | 406 ++++-
.../crash-course/4-components.ipynb | 611 +++++++
.../getting-started/crash-course/4-train.ipynb | 403 -----
.../getting-started/crash-course/5-datasets.ipynb | 452 +++++
.../getting-started/crash-course/5-predict.ipynb | 368 ----
.../getting-started/crash-course/6-train-nn.ipynb | 644 +++++++
.../getting-started/crash-course/6-use_gpus.ipynb | 300 ----
.../getting-started/crash-course/7-use-gpus.ipynb | 412 +++++
.../getting-started/crash-course/index.rst | 22 +-
.../tutorials/performance/backend/amp.ipynb | 2 +-
.../master/api/python/docs/_static/pygments.css | 8 +-
.../master/api/python/docs/api/autograd/index.html | 42 +-
.../master/api/python/docs/api/context/index.html | 42 +-
.../master/api/python/docs/api/contrib/index.html | 42 +-
.../api/python/docs/api/contrib/io/index.html | 42 +-
.../api/python/docs/api/contrib/ndarray/index.html | 175 +-
.../api/python/docs/api/contrib/onnx/index.html | 42 +-
.../api/python/docs/api/contrib/symbol/index.html | 175 +-
.../python/docs/api/contrib/tensorboard/index.html | 42 +-
.../python/docs/api/contrib/tensorrt/index.html | 42 +-
.../api/python/docs/api/contrib/text/index.html | 42 +-
.../master/api/python/docs/api/engine/index.html | 42 +-
.../master/api/python/docs/api/executor/index.html | 42 +-
.../master/api/python/docs/api/gluon/block.html | 42 +-
.../master/api/python/docs/api/gluon/constant.html | 42 +-
.../api/python/docs/api/gluon/contrib/index.html | 42 +-
.../api/python/docs/api/gluon/data/index.html | 42 +-
.../docs/api/gluon/data/vision/datasets/index.html | 42 +-
.../python/docs/api/gluon/data/vision/index.html | 42 +-
.../api/gluon/data/vision/transforms/index.html | 42 +-
.../api/python/docs/api/gluon/hybrid_block.html | 42 +-
.../master/api/python/docs/api/gluon/index.html | 42 +-
.../api/python/docs/api/gluon/loss/index.html | 42 +-
.../api/python/docs/api/gluon/metric/index.html | 42 +-
.../api/python/docs/api/gluon/model_zoo/index.html | 42 +-
.../master/api/python/docs/api/gluon/nn/index.html | 42 +-
.../api/python/docs/api/gluon/parameter.html | 42 +-
.../api/python/docs/api/gluon/rnn/index.html | 42 +-
.../api/python/docs/api/gluon/symbol_block.html | 42 +-
.../master/api/python/docs/api/gluon/trainer.html | 42 +-
.../api/python/docs/api/gluon/utils/index.html | 42 +-
versions/master/api/python/docs/api/index.html | 42 +-
.../api/python/docs/api/initializer/index.html | 42 +-
.../master/api/python/docs/api/kvstore/index.html | 42 +-
.../api/python/docs/api/kvstore_server/index.html | 42 +-
.../api/python/docs/api/legacy/callback/index.html | 42 +-
.../api/python/docs/api/legacy/image/index.html | 42 +-
.../master/api/python/docs/api/legacy/index.html | 42 +-
.../api/python/docs/api/legacy/io/index.html | 42 +-
.../docs/api/legacy/ndarray/contrib/index.html | 175 +-
.../docs/api/legacy/ndarray/image/index.html | 42 +-
.../api/python/docs/api/legacy/ndarray/index.html | 42 +-
.../docs/api/legacy/ndarray/linalg/index.html | 42 +-
.../python/docs/api/legacy/ndarray/ndarray.html | 46 +-
.../python/docs/api/legacy/ndarray/op/index.html | 46 +-
.../docs/api/legacy/ndarray/random/index.html | 42 +-
.../docs/api/legacy/ndarray/register/index.html | 42 +-
.../docs/api/legacy/ndarray/sparse/index.html | 42 +-
.../docs/api/legacy/ndarray/utils/index.html | 42 +-
.../api/python/docs/api/legacy/recordio/index.html | 42 +-
.../docs/api/legacy/symbol/contrib/index.html | 175 +-
.../python/docs/api/legacy/symbol/image/index.html | 42 +-
.../api/python/docs/api/legacy/symbol/index.html | 42 +-
.../docs/api/legacy/symbol/linalg/index.html | 42 +-
.../python/docs/api/legacy/symbol/op/index.html | 46 +-
.../docs/api/legacy/symbol/random/index.html | 42 +-
.../docs/api/legacy/symbol/register/index.html | 42 +-
.../docs/api/legacy/symbol/sparse/index.html | 42 +-
.../api/python/docs/api/legacy/symbol/symbol.html | 46 +-
.../docs/api/legacy/visualization/index.html | 42 +-
.../api/python/docs/api/lr_scheduler/index.html | 42 +-
versions/master/api/python/docs/api/np/arrays.html | 42 +-
.../api/python/docs/api/np/arrays.indexing.html | 42 +-
.../api/python/docs/api/np/arrays.ndarray.html | 42 +-
.../docs/api/np/generated/mxnet.np.absolute.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.add.html | 42 +-
.../docs/api/np/generated/mxnet.np.amax.html | 42 +-
.../docs/api/np/generated/mxnet.np.amin.html | 42 +-
.../docs/api/np/generated/mxnet.np.append.html | 42 +-
.../docs/api/np/generated/mxnet.np.arange.html | 42 +-
.../docs/api/np/generated/mxnet.np.arccos.html | 42 +-
.../docs/api/np/generated/mxnet.np.arccosh.html | 42 +-
.../docs/api/np/generated/mxnet.np.arcsin.html | 42 +-
.../docs/api/np/generated/mxnet.np.arcsinh.html | 42 +-
.../docs/api/np/generated/mxnet.np.arctan.html | 42 +-
.../docs/api/np/generated/mxnet.np.arctan2.html | 42 +-
.../docs/api/np/generated/mxnet.np.arctanh.html | 42 +-
.../docs/api/np/generated/mxnet.np.argmax.html | 42 +-
.../docs/api/np/generated/mxnet.np.argmin.html | 42 +-
.../api/np/generated/mxnet.np.argpartition.html | 42 +-
.../docs/api/np/generated/mxnet.np.argsort.html | 42 +-
.../docs/api/np/generated/mxnet.np.argwhere.html | 42 +-
.../docs/api/np/generated/mxnet.np.around.html | 42 +-
.../docs/api/np/generated/mxnet.np.array.html | 42 +-
.../api/np/generated/mxnet.np.array_split.html | 42 +-
.../docs/api/np/generated/mxnet.np.atleast_1d.html | 42 +-
.../docs/api/np/generated/mxnet.np.atleast_2d.html | 42 +-
.../docs/api/np/generated/mxnet.np.atleast_3d.html | 42 +-
.../docs/api/np/generated/mxnet.np.average.html | 42 +-
.../docs/api/np/generated/mxnet.np.bincount.html | 42 +-
.../np/generated/mxnet.np.broadcast_arrays.html | 42 +-
.../api/np/generated/mxnet.np.broadcast_to.html | 42 +-
.../docs/api/np/generated/mxnet.np.cbrt.html | 42 +-
.../docs/api/np/generated/mxnet.np.ceil.html | 42 +-
.../docs/api/np/generated/mxnet.np.clip.html | 42 +-
.../api/np/generated/mxnet.np.column_stack.html | 42 +-
.../api/np/generated/mxnet.np.concatenate.html | 42 +-
.../docs/api/np/generated/mxnet.np.copy.html | 42 +-
.../docs/api/np/generated/mxnet.np.copysign.html | 42 +-
.../docs/api/np/generated/mxnet.np.corrcoef.html | 42 +-
.../docs/api/np/generated/mxnet.np.correlate.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.cos.html | 42 +-
.../docs/api/np/generated/mxnet.np.cosh.html | 42 +-
.../api/np/generated/mxnet.np.count_nonzero.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.cov.html | 42 +-
.../docs/api/np/generated/mxnet.np.cross.html | 42 +-
.../docs/api/np/generated/mxnet.np.cumprod.html | 42 +-
.../docs/api/np/generated/mxnet.np.cumsum.html | 42 +-
.../docs/api/np/generated/mxnet.np.deg2rad.html | 42 +-
.../docs/api/np/generated/mxnet.np.degrees.html | 42 +-
.../docs/api/np/generated/mxnet.np.delete.html | 42 +-
.../docs/api/np/generated/mxnet.np.diff.html | 42 +-
.../docs/api/np/generated/mxnet.np.digitize.html | 42 +-
.../docs/api/np/generated/mxnet.np.divide.html | 42 +-
.../docs/api/np/generated/mxnet.np.divmod.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.dot.html | 42 +-
.../docs/api/np/generated/mxnet.np.dsplit.html | 42 +-
.../docs/api/np/generated/mxnet.np.dstack.html | 42 +-
.../docs/api/np/generated/mxnet.np.ediff1d.html | 42 +-
.../docs/api/np/generated/mxnet.np.einsum.html | 42 +-
.../docs/api/np/generated/mxnet.np.empty.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.exp.html | 42 +-
.../api/np/generated/mxnet.np.expand_dims.html | 42 +-
.../docs/api/np/generated/mxnet.np.expm1.html | 42 +-
.../docs/api/np/generated/mxnet.np.extract.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.eye.html | 42 +-
.../docs/api/np/generated/mxnet.np.fabs.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.fix.html | 42 +-
.../api/np/generated/mxnet.np.flatnonzero.html | 42 +-
.../docs/api/np/generated/mxnet.np.flip.html | 42 +-
.../docs/api/np/generated/mxnet.np.fliplr.html | 42 +-
.../docs/api/np/generated/mxnet.np.flipud.html | 42 +-
.../api/np/generated/mxnet.np.float_power.html | 42 +-
.../docs/api/np/generated/mxnet.np.floor.html | 42 +-
.../docs/api/np/generated/mxnet.np.fmax.html | 42 +-
.../docs/api/np/generated/mxnet.np.fmin.html | 42 +-
.../docs/api/np/generated/mxnet.np.fmod.html | 42 +-
.../docs/api/np/generated/mxnet.np.frexp.html | 42 +-
.../docs/api/np/generated/mxnet.np.full.html | 42 +-
.../docs/api/np/generated/mxnet.np.genfromtxt.html | 42 +-
.../docs/api/np/generated/mxnet.np.heaviside.html | 42 +-
.../docs/api/np/generated/mxnet.np.histogram.html | 42 +-
.../api/np/generated/mxnet.np.histogram2d.html | 42 +-
.../np/generated/mxnet.np.histogram_bin_edges.html | 42 +-
.../api/np/generated/mxnet.np.histogramdd.html | 42 +-
.../docs/api/np/generated/mxnet.np.hsplit.html | 42 +-
.../docs/api/np/generated/mxnet.np.hstack.html | 42 +-
.../docs/api/np/generated/mxnet.np.hypot.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.i0.html | 42 +-
.../docs/api/np/generated/mxnet.np.identity.html | 42 +-
.../docs/api/np/generated/mxnet.np.inner.html | 42 +-
.../docs/api/np/generated/mxnet.np.insert.html | 42 +-
.../docs/api/np/generated/mxnet.np.interp.html | 42 +-
.../docs/api/np/generated/mxnet.np.kron.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.lcm.html | 42 +-
.../docs/api/np/generated/mxnet.np.ldexp.html | 42 +-
.../docs/api/np/generated/mxnet.np.lexsort.html | 42 +-
.../api/np/generated/mxnet.np.linalg.cholesky.html | 42 +-
.../api/np/generated/mxnet.np.linalg.cond.html | 42 +-
.../docs/api/np/generated/mxnet.np.linalg.det.html | 42 +-
.../docs/api/np/generated/mxnet.np.linalg.eig.html | 42 +-
.../api/np/generated/mxnet.np.linalg.eigh.html | 42 +-
.../api/np/generated/mxnet.np.linalg.eigvals.html | 42 +-
.../api/np/generated/mxnet.np.linalg.eigvalsh.html | 42 +-
.../docs/api/np/generated/mxnet.np.linalg.inv.html | 42 +-
.../api/np/generated/mxnet.np.linalg.lstsq.html | 42 +-
.../np/generated/mxnet.np.linalg.matrix_power.html | 42 +-
.../np/generated/mxnet.np.linalg.matrix_rank.html | 42 +-
.../np/generated/mxnet.np.linalg.multi_dot.html | 42 +-
.../api/np/generated/mxnet.np.linalg.norm.html | 42 +-
.../api/np/generated/mxnet.np.linalg.pinv.html | 42 +-
.../docs/api/np/generated/mxnet.np.linalg.qr.html | 42 +-
.../api/np/generated/mxnet.np.linalg.slogdet.html | 42 +-
.../api/np/generated/mxnet.np.linalg.solve.html | 42 +-
.../docs/api/np/generated/mxnet.np.linalg.svd.html | 42 +-
.../np/generated/mxnet.np.linalg.tensorinv.html | 42 +-
.../np/generated/mxnet.np.linalg.tensorsolve.html | 42 +-
.../docs/api/np/generated/mxnet.np.linspace.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.log.html | 42 +-
.../docs/api/np/generated/mxnet.np.log10.html | 42 +-
.../docs/api/np/generated/mxnet.np.log1p.html | 42 +-
.../docs/api/np/generated/mxnet.np.log2.html | 42 +-
.../docs/api/np/generated/mxnet.np.logspace.html | 42 +-
.../docs/api/np/generated/mxnet.np.matmul.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.max.html | 42 +-
.../docs/api/np/generated/mxnet.np.maximum.html | 42 +-
.../docs/api/np/generated/mxnet.np.mean.html | 42 +-
.../docs/api/np/generated/mxnet.np.median.html | 42 +-
.../docs/api/np/generated/mxnet.np.meshgrid.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.min.html | 42 +-
.../docs/api/np/generated/mxnet.np.minimum.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.mod.html | 42 +-
.../docs/api/np/generated/mxnet.np.modf.html | 42 +-
.../docs/api/np/generated/mxnet.np.moveaxis.html | 42 +-
.../docs/api/np/generated/mxnet.np.msort.html | 42 +-
.../docs/api/np/generated/mxnet.np.multiply.html | 42 +-
.../docs/api/np/generated/mxnet.np.nan_to_num.html | 42 +-
.../docs/api/np/generated/mxnet.np.nanargmax.html | 42 +-
.../docs/api/np/generated/mxnet.np.nanargmin.html | 42 +-
.../docs/api/np/generated/mxnet.np.nancumprod.html | 42 +-
.../docs/api/np/generated/mxnet.np.nancumsum.html | 42 +-
.../docs/api/np/generated/mxnet.np.nanmax.html | 42 +-
.../docs/api/np/generated/mxnet.np.nanmedian.html | 42 +-
.../docs/api/np/generated/mxnet.np.nanmin.html | 42 +-
.../api/np/generated/mxnet.np.nanpercentile.html | 42 +-
.../docs/api/np/generated/mxnet.np.nanprod.html | 42 +-
.../api/np/generated/mxnet.np.nanquantile.html | 42 +-
.../docs/api/np/generated/mxnet.np.nanstd.html | 42 +-
.../docs/api/np/generated/mxnet.np.nansum.html | 42 +-
.../docs/api/np/generated/mxnet.np.nanvar.html | 42 +-
.../docs/api/np/generated/mxnet.np.ndarray.T.html | 42 +-
.../api/np/generated/mxnet.np.ndarray.flatten.html | 42 +-
.../api/np/generated/mxnet.np.ndarray.sort.html | 42 +-
.../api/np/generated/mxnet.np.ndarray.tolist.html | 42 +-
.../docs/api/np/generated/mxnet.np.negative.html | 42 +-
.../docs/api/np/generated/mxnet.np.nonzero.html | 42 +-
.../docs/api/np/generated/mxnet.np.ones.html | 42 +-
.../docs/api/np/generated/mxnet.np.ones_like.html | 42 +-
.../docs/api/np/generated/mxnet.np.outer.html | 42 +-
.../docs/api/np/generated/mxnet.np.partition.html | 42 +-
.../docs/api/np/generated/mxnet.np.percentile.html | 42 +-
.../docs/api/np/generated/mxnet.np.positive.html | 42 +-
.../docs/api/np/generated/mxnet.np.power.html | 42 +-
.../docs/api/np/generated/mxnet.np.prod.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.ptp.html | 42 +-
.../docs/api/np/generated/mxnet.np.quantile.html | 42 +-
.../docs/api/np/generated/mxnet.np.rad2deg.html | 42 +-
.../docs/api/np/generated/mxnet.np.radians.html | 42 +-
.../docs/api/np/generated/mxnet.np.ravel.html | 42 +-
.../docs/api/np/generated/mxnet.np.reciprocal.html | 42 +-
.../docs/api/np/generated/mxnet.np.remainder.html | 42 +-
.../docs/api/np/generated/mxnet.np.repeat.html | 42 +-
.../docs/api/np/generated/mxnet.np.reshape.html | 42 +-
.../docs/api/np/generated/mxnet.np.resize.html | 42 +-
.../docs/api/np/generated/mxnet.np.rint.html | 42 +-
.../docs/api/np/generated/mxnet.np.roll.html | 42 +-
.../docs/api/np/generated/mxnet.np.rollaxis.html | 42 +-
.../docs/api/np/generated/mxnet.np.rot90.html | 42 +-
.../docs/api/np/generated/mxnet.np.round_.html | 42 +-
.../api/np/generated/mxnet.np.searchsorted.html | 42 +-
.../np/generated/mxnet.np.set_printoptions.html | 42 +-
.../docs/api/np/generated/mxnet.np.sign.html | 42 +-
.../docs/api/np/generated/mxnet.np.signbit.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.sin.html | 42 +-
.../docs/api/np/generated/mxnet.np.sinh.html | 42 +-
.../docs/api/np/generated/mxnet.np.sort.html | 42 +-
.../docs/api/np/generated/mxnet.np.spacing.html | 42 +-
.../docs/api/np/generated/mxnet.np.split.html | 42 +-
.../docs/api/np/generated/mxnet.np.sqrt.html | 42 +-
.../docs/api/np/generated/mxnet.np.square.html | 42 +-
.../docs/api/np/generated/mxnet.np.squeeze.html | 42 +-
.../docs/api/np/generated/mxnet.np.stack.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.std.html | 42 +-
.../docs/api/np/generated/mxnet.np.subtract.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.sum.html | 42 +-
.../docs/api/np/generated/mxnet.np.swapaxes.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.tan.html | 42 +-
.../docs/api/np/generated/mxnet.np.tanh.html | 42 +-
.../docs/api/np/generated/mxnet.np.tensordot.html | 42 +-
.../docs/api/np/generated/mxnet.np.tile.html | 42 +-
.../docs/api/np/generated/mxnet.np.trace.html | 42 +-
.../docs/api/np/generated/mxnet.np.transpose.html | 42 +-
.../docs/api/np/generated/mxnet.np.trapz.html | 42 +-
.../docs/api/np/generated/mxnet.np.tril.html | 42 +-
.../docs/api/np/generated/mxnet.np.trim_zeros.html | 42 +-
.../api/np/generated/mxnet.np.true_divide.html | 42 +-
.../docs/api/np/generated/mxnet.np.trunc.html | 42 +-
.../docs/api/np/generated/mxnet.np.unique.html | 42 +-
.../docs/api/np/generated/mxnet.np.unwrap.html | 42 +-
.../python/docs/api/np/generated/mxnet.np.var.html | 42 +-
.../docs/api/np/generated/mxnet.np.vdot.html | 42 +-
.../docs/api/np/generated/mxnet.np.vsplit.html | 42 +-
.../docs/api/np/generated/mxnet.np.vstack.html | 42 +-
.../docs/api/np/generated/mxnet.np.where.html | 42 +-
.../docs/api/np/generated/mxnet.np.zeros.html | 42 +-
.../docs/api/np/generated/mxnet.np.zeros_like.html | 42 +-
versions/master/api/python/docs/api/np/index.html | 42 +-
.../np/random/generated/mxnet.np.random.beta.html | 42 +-
.../generated/mxnet.np.random.chisquare.html | 42 +-
.../random/generated/mxnet.np.random.choice.html | 42 +-
.../generated/mxnet.np.random.exponential.html | 42 +-
.../api/np/random/generated/mxnet.np.random.f.html | 42 +-
.../np/random/generated/mxnet.np.random.gamma.html | 42 +-
.../random/generated/mxnet.np.random.gumbel.html | 42 +-
.../random/generated/mxnet.np.random.laplace.html | 42 +-
.../random/generated/mxnet.np.random.logistic.html | 42 +-
.../generated/mxnet.np.random.lognormal.html | 42 +-
.../generated/mxnet.np.random.multinomial.html | 42 +-
.../mxnet.np.random.multivariate_normal.html | 42 +-
.../random/generated/mxnet.np.random.normal.html | 42 +-
.../random/generated/mxnet.np.random.pareto.html | 42 +-
.../np/random/generated/mxnet.np.random.power.html | 42 +-
.../np/random/generated/mxnet.np.random.rand.html | 42 +-
.../random/generated/mxnet.np.random.randint.html | 42 +-
.../random/generated/mxnet.np.random.rayleigh.html | 42 +-
.../random/generated/mxnet.np.random.shuffle.html | 42 +-
.../random/generated/mxnet.np.random.uniform.html | 42 +-
.../random/generated/mxnet.np.random.weibull.html | 42 +-
.../api/python/docs/api/np/random/index.html | 42 +-
.../docs/api/np/routines.array-creation.html | 42 +-
.../docs/api/np/routines.array-manipulation.html | 42 +-
.../master/api/python/docs/api/np/routines.html | 42 +-
.../master/api/python/docs/api/np/routines.io.html | 42 +-
.../api/python/docs/api/np/routines.linalg.html | 42 +-
.../api/python/docs/api/np/routines.math.html | 42 +-
.../api/python/docs/api/np/routines.sort.html | 42 +-
.../python/docs/api/np/routines.statistics.html | 42 +-
.../api/npx/generated/mxnet.npx.activation.html | 42 +-
.../api/npx/generated/mxnet.npx.batch_dot.html | 42 +-
.../api/npx/generated/mxnet.npx.batch_flatten.html | 42 +-
.../api/npx/generated/mxnet.npx.batch_norm.html | 42 +-
.../api/npx/generated/mxnet.npx.convolution.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.cpu.html | 42 +-
.../api/npx/generated/mxnet.npx.cpu_pinned.html | 42 +-
.../npx/generated/mxnet.npx.current_context.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.dropout.html | 42 +-
.../api/npx/generated/mxnet.npx.embedding.html | 42 +-
.../npx/generated/mxnet.npx.fully_connected.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.gamma.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.gpu.html | 42 +-
.../npx/generated/mxnet.npx.gpu_memory_info.html | 42 +-
.../api/npx/generated/mxnet.npx.layer_norm.html | 42 +-
.../api/npx/generated/mxnet.npx.leaky_relu.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.load.html | 42 +-
.../generated/mxnet.npx.multibox_detection.html | 42 +-
.../npx/generated/mxnet.npx.multibox_prior.html | 42 +-
.../npx/generated/mxnet.npx.multibox_target.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.num_gpus.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.one_hot.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.pick.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.pooling.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.reset_np.html | 42 +-
.../api/npx/generated/mxnet.npx.reshape_like.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.rnn.html | 42 +-
.../api/npx/generated/mxnet.npx.roi_pooling.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.save.html | 42 +-
.../api/npx/generated/mxnet.npx.sequence_mask.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.set_np.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.sigmoid.html | 42 +-
.../api/npx/generated/mxnet.npx.smooth_l1.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.softmax.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.topk.html | 42 +-
.../docs/api/npx/generated/mxnet.npx.waitall.html | 42 +-
versions/master/api/python/docs/api/npx/index.html | 42 +-
.../api/python/docs/api/optimizer/index.html | 42 +-
.../master/api/python/docs/api/profiler/index.html | 42 +-
versions/master/api/python/docs/api/rtc/index.html | 42 +-
.../runtime/generated/mxnet.runtime.Feature.html | 42 +-
.../runtime/generated/mxnet.runtime.Features.html | 42 +-
.../generated/mxnet.runtime.feature_list.html | 42 +-
.../master/api/python/docs/api/runtime/index.html | 42 +-
.../api/python/docs/api/test_utils/index.html | 42 +-
.../master/api/python/docs/api/util/index.html | 42 +-
versions/master/api/python/docs/genindex.html | 76 +-
versions/master/api/python/docs/index.html | 42 +-
versions/master/api/python/docs/objects.inv | Bin 89363 -> 90871 bytes
versions/master/api/python/docs/py-modindex.html | 42 +-
versions/master/api/python/docs/search.html | 42 +-
versions/master/api/python/docs/searchindex.js | 2 +-
.../python/docs/tutorials/deploy/export/index.html | 42 +-
.../docs/tutorials/deploy/export/index.html.bak | 42 +-
.../python/docs/tutorials/deploy/export/onnx.html | 42 +-
.../docs/tutorials/deploy/export/onnx.html.bak | 42 +-
.../api/python/docs/tutorials/deploy/index.html | 42 +-
.../python/docs/tutorials/deploy/index.html.bak | 42 +-
.../docs/tutorials/deploy/inference/cpp.html | 42 +-
.../docs/tutorials/deploy/inference/cpp.html.bak | 42 +-
.../inference/image_classification_jetson.html | 42 +-
.../inference/image_classification_jetson.html.bak | 42 +-
.../docs/tutorials/deploy/inference/index.html | 42 +-
.../docs/tutorials/deploy/inference/index.html.bak | 42 +-
.../docs/tutorials/deploy/run-on-aws/cloud.html | 42 +-
.../tutorials/deploy/run-on-aws/cloud.html.bak | 42 +-
.../docs/tutorials/deploy/run-on-aws/index.html | 42 +-
.../tutorials/deploy/run-on-aws/index.html.bak | 42 +-
.../docs/tutorials/deploy/run-on-aws/use_ec2.html | 42 +-
.../tutorials/deploy/run-on-aws/use_ec2.html.bak | 42 +-
.../tutorials/deploy/run-on-aws/use_sagemaker.html | 42 +-
.../deploy/run-on-aws/use_sagemaker.html.bak | 42 +-
.../api/python/docs/tutorials/extend/customop.html | 42 +-
.../python/docs/tutorials/extend/customop.html.bak | 42 +-
.../api/python/docs/tutorials/extend/index.html | 42 +-
.../python/docs/tutorials/extend/index.html.bak | 42 +-
.../{index.html => 0-introduction.html} | 159 +-
.../{index.html => 0-introduction.html.bak} | 159 +-
.../getting-started/crash-course/1-nparray.html | 405 +----
.../{1-ndarray.html.bak => 1-nparray.html.bak} | 405 +----
.../crash-course/{2-nn.html => 2-create-nn.html} | 830 +++++----
.../{2-nn.html.bak => 2-create-nn.html.bak} | 830 +++++----
.../getting-started/crash-course/3-autograd.html | 443 ++---
.../crash-course/3-autograd.html.bak | 443 ++---
.../getting-started/crash-course/4-components.html | 636 +++----
.../{4-train.html.bak => 4-components.html.bak} | 636 +++----
.../getting-started/crash-course/5-datasets.html | 606 +++----
.../crash-course/5-datasets.html.bak | 606 +++----
.../getting-started/crash-course/5-predict.html | 1813 --------------------
.../crash-course/5-predict.html.bak | 1813 --------------------
.../getting-started/crash-course/6-train-nn.html | 512 ++++--
.../crash-course/6-train-nn.html.bak | 512 ++++--
.../getting-started/crash-course/7-use-gpus.html | 269 ++-
.../crash-course/7-use-gpus.html.bak | 269 ++-
.../getting-started/crash-course/index.html | 79 +-
.../getting-started/crash-course/index.html.bak | 79 +-
.../gluon_from_experiment_to_deployment.html | 42 +-
.../gluon_from_experiment_to_deployment.html.bak | 42 +-
.../docs/tutorials/getting-started/index.html | 46 +-
.../docs/tutorials/getting-started/index.html.bak | 46 +-
.../logistic_regression_explained.html | 42 +-
.../logistic_regression_explained.html.bak | 42 +-
.../tutorials/getting-started/to-mxnet/index.html | 48 +-
.../getting-started/to-mxnet/index.html.bak | 48 +-
.../getting-started/to-mxnet/pytorch.html | 42 +-
.../getting-started/to-mxnet/pytorch.html.bak | 42 +-
.../master/api/python/docs/tutorials/index.html | 42 +-
.../api/python/docs/tutorials/index.html.bak | 42 +-
.../docs/tutorials/packages/autograd/index.html | 42 +-
.../tutorials/packages/autograd/index.html.bak | 42 +-
.../gluon/blocks/activations/activations.html | 42 +-
.../gluon/blocks/activations/activations.html.bak | 42 +-
.../packages/gluon/blocks/custom-layer.html | 42 +-
.../packages/gluon/blocks/custom-layer.html.bak | 42 +-
.../gluon/blocks/custom_layer_beginners.html | 42 +-
.../gluon/blocks/custom_layer_beginners.html.bak | 42 +-
.../tutorials/packages/gluon/blocks/hybridize.html | 42 +-
.../packages/gluon/blocks/hybridize.html.bak | 42 +-
.../tutorials/packages/gluon/blocks/index.html | 42 +-
.../tutorials/packages/gluon/blocks/index.html.bak | 42 +-
.../docs/tutorials/packages/gluon/blocks/init.html | 42 +-
.../tutorials/packages/gluon/blocks/init.html.bak | 42 +-
.../tutorials/packages/gluon/blocks/naming.html | 42 +-
.../packages/gluon/blocks/naming.html.bak | 42 +-
.../docs/tutorials/packages/gluon/blocks/nn.html | 42 +-
.../tutorials/packages/gluon/blocks/nn.html.bak | 42 +-
.../packages/gluon/blocks/parameters.html | 42 +-
.../packages/gluon/blocks/parameters.html.bak | 42 +-
.../packages/gluon/blocks/save_load_params.html | 42 +-
.../gluon/blocks/save_load_params.html.bak | 42 +-
.../packages/gluon/data/data_augmentation.html | 42 +-
.../packages/gluon/data/data_augmentation.html.bak | 42 +-
.../tutorials/packages/gluon/data/datasets.html | 42 +-
.../packages/gluon/data/datasets.html.bak | 42 +-
.../docs/tutorials/packages/gluon/data/index.html | 42 +-
.../tutorials/packages/gluon/data/index.html.bak | 42 +-
.../docs/tutorials/packages/gluon/image/index.html | 42 +-
.../tutorials/packages/gluon/image/index.html.bak | 42 +-
.../tutorials/packages/gluon/image/info_gan.html | 42 +-
.../packages/gluon/image/info_gan.html.bak | 42 +-
.../docs/tutorials/packages/gluon/image/mnist.html | 42 +-
.../tutorials/packages/gluon/image/mnist.html.bak | 42 +-
.../docs/tutorials/packages/gluon/index.html | 42 +-
.../docs/tutorials/packages/gluon/index.html.bak | 42 +-
.../tutorials/packages/gluon/loss/custom-loss.html | 42 +-
.../packages/gluon/loss/custom-loss.html.bak | 42 +-
.../docs/tutorials/packages/gluon/loss/index.html | 42 +-
.../tutorials/packages/gluon/loss/index.html.bak | 42 +-
.../packages/gluon/loss/kl_divergence.html | 42 +-
.../packages/gluon/loss/kl_divergence.html.bak | 42 +-
.../docs/tutorials/packages/gluon/loss/loss.html | 42 +-
.../tutorials/packages/gluon/loss/loss.html.bak | 42 +-
.../docs/tutorials/packages/gluon/text/gnmt.html | 42 +-
.../tutorials/packages/gluon/text/gnmt.html.bak | 42 +-
.../docs/tutorials/packages/gluon/text/index.html | 42 +-
.../tutorials/packages/gluon/text/index.html.bak | 42 +-
.../tutorials/packages/gluon/text/transformer.html | 42 +-
.../packages/gluon/text/transformer.html.bak | 42 +-
.../packages/gluon/training/fit_api_tutorial.html | 42 +-
.../gluon/training/fit_api_tutorial.html.bak | 42 +-
.../tutorials/packages/gluon/training/index.html | 42 +-
.../packages/gluon/training/index.html.bak | 42 +-
.../gluon/training/learning_rates/index.html | 42 +-
.../gluon/training/learning_rates/index.html.bak | 42 +-
.../learning_rates/learning_rate_finder.html | 42 +-
.../learning_rates/learning_rate_finder.html.bak | 42 +-
.../learning_rates/learning_rate_schedules.html | 42 +-
.../learning_rate_schedules.html.bak | 42 +-
.../learning_rate_schedules_advanced.html | 42 +-
.../learning_rate_schedules_advanced.html.bak | 42 +-
.../gluon/training/normalization/index.html | 42 +-
.../gluon/training/normalization/index.html.bak | 42 +-
.../tutorials/packages/gluon/training/trainer.html | 42 +-
.../packages/gluon/training/trainer.html.bak | 42 +-
.../api/python/docs/tutorials/packages/index.html | 42 +-
.../python/docs/tutorials/packages/index.html.bak | 42 +-
.../docs/tutorials/packages/kvstore/index.html | 42 +-
.../docs/tutorials/packages/kvstore/index.html.bak | 42 +-
.../docs/tutorials/packages/kvstore/kvstore.html | 42 +-
.../tutorials/packages/kvstore/kvstore.html.bak | 42 +-
.../docs/tutorials/packages/legacy/index.html | 42 +-
.../docs/tutorials/packages/legacy/index.html.bak | 42 +-
.../packages/legacy/ndarray/01-ndarray-intro.html | 42 +-
.../legacy/ndarray/01-ndarray-intro.html.bak | 42 +-
.../legacy/ndarray/02-ndarray-operations.html | 42 +-
.../legacy/ndarray/02-ndarray-operations.html.bak | 42 +-
.../legacy/ndarray/03-ndarray-contexts.html | 42 +-
.../legacy/ndarray/03-ndarray-contexts.html.bak | 42 +-
.../legacy/ndarray/gotchas_numpy_in_mxnet.html | 42 +-
.../legacy/ndarray/gotchas_numpy_in_mxnet.html.bak | 42 +-
.../tutorials/packages/legacy/ndarray/index.html | 42 +-
.../packages/legacy/ndarray/index.html.bak | 42 +-
.../packages/legacy/ndarray/sparse/csr.html | 42 +-
.../packages/legacy/ndarray/sparse/csr.html.bak | 42 +-
.../packages/legacy/ndarray/sparse/index.html | 42 +-
.../packages/legacy/ndarray/sparse/index.html.bak | 42 +-
.../packages/legacy/ndarray/sparse/row_sparse.html | 42 +-
.../legacy/ndarray/sparse/row_sparse.html.bak | 42 +-
.../legacy/ndarray/sparse/train_gluon.html | 42 +-
.../legacy/ndarray/sparse/train_gluon.html.bak | 42 +-
.../docs/tutorials/packages/np/cheat-sheet.html | 42 +-
.../tutorials/packages/np/cheat-sheet.html.bak | 42 +-
.../python/docs/tutorials/packages/np/index.html | 42 +-
.../docs/tutorials/packages/np/index.html.bak | 42 +-
.../docs/tutorials/packages/np/np-vs-numpy.html | 42 +-
.../tutorials/packages/np/np-vs-numpy.html.bak | 42 +-
.../tutorials/packages/onnx/fine_tuning_gluon.html | 42 +-
.../packages/onnx/fine_tuning_gluon.html.bak | 42 +-
.../python/docs/tutorials/packages/onnx/index.html | 42 +-
.../docs/tutorials/packages/onnx/index.html.bak | 42 +-
.../packages/onnx/inference_on_onnx_model.html | 42 +-
.../packages/onnx/inference_on_onnx_model.html.bak | 42 +-
.../docs/tutorials/packages/optimizer/index.html | 42 +-
.../tutorials/packages/optimizer/index.html.bak | 42 +-
.../python/docs/tutorials/packages/viz/index.html | 42 +-
.../docs/tutorials/packages/viz/index.html.bak | 42 +-
.../docs/tutorials/performance/backend/amp.html | 44 +-
.../tutorials/performance/backend/amp.html.bak | 44 +-
.../docs/tutorials/performance/backend/index.html | 42 +-
.../tutorials/performance/backend/index.html.bak | 42 +-
.../performance/backend/mkldnn/index.html | 42 +-
.../performance/backend/mkldnn/index.html.bak | 42 +-
.../performance/backend/mkldnn/mkldnn_readme.html | 42 +-
.../backend/mkldnn/mkldnn_readme.html.bak | 42 +-
.../tutorials/performance/backend/profiler.html | 42 +-
.../performance/backend/profiler.html.bak | 42 +-
.../docs/tutorials/performance/backend/tvm.html | 42 +-
.../tutorials/performance/backend/tvm.html.bak | 42 +-
.../tutorials/performance/compression/index.html | 42 +-
.../performance/compression/index.html.bak | 42 +-
.../tutorials/performance/compression/int8.html | 42 +-
.../performance/compression/int8.html.bak | 42 +-
.../python/docs/tutorials/performance/index.html | 42 +-
.../docs/tutorials/performance/index.html.bak | 42 +-
versions/master/feed.xml | 2 +-
1295 files changed, 52552 insertions(+), 33471 deletions(-)
diff --git a/api/clojure/docs/tutorials.html b/api/clojure/docs/tutorials.html
index cdb467d..1cd75f8 100644
--- a/api/clojure/docs/tutorials.html
+++ b/api/clojure/docs/tutorials.html
@@ -578,12 +578,12 @@ if(!(window.doNotTrack === "1" || navigator.doNotTrack === "1" || navigator.doNo
<!-- resource-p -->
+
+ <li><a href="/versions/master/api/clojure/docs/tutorials/kvstore">KVStore API</a></li>
<!-- page-category -->
<!-- resource-p -->
-
- <li><a href="/versions/master/api/clojure/docs/tutorials/kvstore">KVStore API</a></li>
<!-- page-category -->
<!-- resource-p -->
@@ -620,12 +620,12 @@ if(!(window.doNotTrack === "1" || navigator.doNotTrack === "1" || navigator.doNo
<!-- resource-p -->
+
+ <li><a href="/versions/master/api/clojure/docs/tutorials/ndarray">NDArray</a></li>
<!-- page-category -->
<!-- resource-p -->
-
- <li><a href="/versions/master/api/clojure/docs/tutorials/ndarray">NDArray</a></li>
<!-- page-category -->
<!-- resource-p -->
@@ -670,12 +670,12 @@ if(!(window.doNotTrack === "1" || navigator.doNotTrack === "1" || navigator.doNo
<!-- resource-p -->
+
+ <li><a href="/versions/master/api/clojure/docs/tutorials/symbol">Symbolic API</a></li>
<!-- page-category -->
<!-- resource-p -->
-
- <li><a href="/versions/master/api/clojure/docs/tutorials/symbol">Symbolic API</a></li>
<!-- page-category -->
<!-- resource-p -->
diff --git a/api/perl/docs/tutorials.html b/api/perl/docs/tutorials.html
index 4c71166..63ea967 100644
--- a/api/perl/docs/tutorials.html
+++ b/api/perl/docs/tutorials.html
@@ -580,12 +580,12 @@ if(!(window.doNotTrack === "1" || navigator.doNotTrack === "1" || navigator.doNo
<!-- resource-p -->
-
- <li><a href="/versions/master/api/perl/docs/tutorials/kvstore">KVStore API</a></li>
<!-- page-category -->
<!-- resource-p -->
+
+ <li><a href="/versions/master/api/perl/docs/tutorials/kvstore">KVStore API</a></li>
<!-- page-category -->
<!-- resource-p -->
@@ -620,12 +620,12 @@ if(!(window.doNotTrack === "1" || navigator.doNotTrack === "1" || navigator.doNo
<!-- resource-p -->
-
- <li><a href="/versions/master/api/perl/docs/tutorials/ndarray">NDArray API</a></li>
<!-- page-category -->
<!-- resource-p -->
+
+ <li><a href="/versions/master/api/perl/docs/tutorials/ndarray">NDArray API</a></li>
<!-- page-category -->
<!-- resource-p -->
@@ -670,12 +670,12 @@ if(!(window.doNotTrack === "1" || navigator.doNotTrack === "1" || navigator.doNo
<!-- resource-p -->
-
- <li><a href="/versions/master/api/perl/docs/tutorials/symbol">Symbol API</a></li>
<!-- page-category -->
<!-- resource-p -->
+
+ <li><a href="/versions/master/api/perl/docs/tutorials/symbol">Symbol API</a></li>
<!-- page-category -->
<!-- resource-p -->
diff --git a/api/python/docs/_modules/index.html b/api/python/docs/_modules/index.html
index 82d7cd5..3071d7d7 100644
--- a/api/python/docs/_modules/index.html
+++ b/api/python/docs/_modules/index.html
@@ -174,13 +174,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -749,13 +758,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/autograd.html b/api/python/docs/_modules/mxnet/autograd.html
index 94c976d..b6b4f0c 100644
--- a/api/python/docs/_modules/mxnet/autograd.html
+++ b/api/python/docs/_modules/mxnet/autograd.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/callback.html b/api/python/docs/_modules/mxnet/callback.html
index aaf645d..eacf122 100644
--- a/api/python/docs/_modules/mxnet/callback.html
+++ b/api/python/docs/_modules/mxnet/callback.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/context.html b/api/python/docs/_modules/mxnet/context.html
index 8a04468..99da5b3 100644
--- a/api/python/docs/_modules/mxnet/context.html
+++ b/api/python/docs/_modules/mxnet/context.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/contrib/io.html b/api/python/docs/_modules/mxnet/contrib/io.html
index 96c7ffd..6d15072 100644
--- a/api/python/docs/_modules/mxnet/contrib/io.html
+++ b/api/python/docs/_modules/mxnet/contrib/io.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/contrib/ndarray.html b/api/python/docs/_modules/mxnet/contrib/ndarray.html
index d194a1b..ec07876 100644
--- a/api/python/docs/_modules/mxnet/contrib/ndarray.html
+++ b/api/python/docs/_modules/mxnet/contrib/ndarray.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/contrib/onnx/mx2onnx/export_model.html b/api/python/docs/_modules/mxnet/contrib/onnx/mx2onnx/export_model.html
index be5fbd8..6a2b427 100644
--- a/api/python/docs/_modules/mxnet/contrib/onnx/mx2onnx/export_model.html
+++ b/api/python/docs/_modules/mxnet/contrib/onnx/mx2onnx/export_model.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/contrib/onnx/onnx2mx/import_model.html b/api/python/docs/_modules/mxnet/contrib/onnx/onnx2mx/import_model.html
index 9df040c..21ba132 100644
--- a/api/python/docs/_modules/mxnet/contrib/onnx/onnx2mx/import_model.html
+++ b/api/python/docs/_modules/mxnet/contrib/onnx/onnx2mx/import_model.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/contrib/onnx/onnx2mx/import_to_gluon.html b/api/python/docs/_modules/mxnet/contrib/onnx/onnx2mx/import_to_gluon.html
index aa5cf84..ab2660c 100644
--- a/api/python/docs/_modules/mxnet/contrib/onnx/onnx2mx/import_to_gluon.html
+++ b/api/python/docs/_modules/mxnet/contrib/onnx/onnx2mx/import_to_gluon.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/contrib/symbol.html b/api/python/docs/_modules/mxnet/contrib/symbol.html
index 9a6f508..06432a4 100644
--- a/api/python/docs/_modules/mxnet/contrib/symbol.html
+++ b/api/python/docs/_modules/mxnet/contrib/symbol.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/contrib/tensorboard.html b/api/python/docs/_modules/mxnet/contrib/tensorboard.html
index db43fa3..5894c99 100644
--- a/api/python/docs/_modules/mxnet/contrib/tensorboard.html
+++ b/api/python/docs/_modules/mxnet/contrib/tensorboard.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/contrib/tensorrt.html b/api/python/docs/_modules/mxnet/contrib/tensorrt.html
index 3f00eef..1862643 100644
--- a/api/python/docs/_modules/mxnet/contrib/tensorrt.html
+++ b/api/python/docs/_modules/mxnet/contrib/tensorrt.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/dlpack.html b/api/python/docs/_modules/mxnet/dlpack.html
index 68a5551..4a649e4 100644
--- a/api/python/docs/_modules/mxnet/dlpack.html
+++ b/api/python/docs/_modules/mxnet/dlpack.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/engine.html b/api/python/docs/_modules/mxnet/engine.html
index 6dbe1ba..1ffaadf 100644
--- a/api/python/docs/_modules/mxnet/engine.html
+++ b/api/python/docs/_modules/mxnet/engine.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/executor.html b/api/python/docs/_modules/mxnet/executor.html
index 4ac10f71..9bee90c 100644
--- a/api/python/docs/_modules/mxnet/executor.html
+++ b/api/python/docs/_modules/mxnet/executor.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/block.html b/api/python/docs/_modules/mxnet/gluon/block.html
index 6d274d5..50f5f77 100644
--- a/api/python/docs/_modules/mxnet/gluon/block.html
+++ b/api/python/docs/_modules/mxnet/gluon/block.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -2358,6 +2376,10 @@ Edit on Github
<span class="c1"># Partition the graph.</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">optimize_for</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_backend</span><span class="p">,</span> <span class="n">arg_dict</span><span class="p">,</span> <span class="n">aux_dict</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</ [...]
+ <span class="c1"># convert to numpy symbol if needed</span>
+ <span class="k">if</span> <span class="n">_mx_npx</span><span class="o">.</span><span class="n">is_np_array</span><span class="p">():</span>
+ <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span>
+
<span class="c1">#update cached graph with partitioned graph</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span> <span class="o">=</span> <span class="n">data</span><span class="p">,</span> <span class="n">out</span>
@@ -2392,7 +2414,7 @@ Edit on Github
<span class="s1">'added to the parameter dicts.</span><span class="se">\n</span><span class="s1">'</span>
<span class="s1">'Please check the backend.'</span><span class="p">)</span>
- <span class="n">param</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+ <span class="n">param</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">param_data</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="n">param</span><span class="o">.</span><span class="n">_var_name</span> <span class="o">=</span> <span class="n">name</span>
<span class="n">serialization_name</span> <span class="o">=</span> <span class="n">name</span> <span class="c1"># HybridBlock.export</span>
<span class="n">param</span><span class="o">.</span><span class="n">_load_init</span><span class="p">(</span><span class="n">param_data</span><span class="p">,</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">context</span><span class="p">)</span>
diff --git a/api/python/docs/_modules/mxnet/gluon/contrib/estimator/batch_processor.html b/api/python/docs/_modules/mxnet/gluon/contrib/estimator/batch_processor.html
index dc312ca..eaffbb0 100644
--- a/api/python/docs/_modules/mxnet/gluon/contrib/estimator/batch_processor.html
+++ b/api/python/docs/_modules/mxnet/gluon/contrib/estimator/batch_processor.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/contrib/estimator/estimator.html b/api/python/docs/_modules/mxnet/gluon/contrib/estimator/estimator.html
index 56c5cb8..42cb198 100644
--- a/api/python/docs/_modules/mxnet/gluon/contrib/estimator/estimator.html
+++ b/api/python/docs/_modules/mxnet/gluon/contrib/estimator/estimator.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/contrib/estimator/event_handler.html b/api/python/docs/_modules/mxnet/gluon/contrib/estimator/event_handler.html
index 7752cef..06118f9 100644
--- a/api/python/docs/_modules/mxnet/gluon/contrib/estimator/event_handler.html
+++ b/api/python/docs/_modules/mxnet/gluon/contrib/estimator/event_handler.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/data/dataloader.html b/api/python/docs/_modules/mxnet/gluon/data/dataloader.html
index 07c0d80..d37887a1 100644
--- a/api/python/docs/_modules/mxnet/gluon/data/dataloader.html
+++ b/api/python/docs/_modules/mxnet/gluon/data/dataloader.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/data/dataset.html b/api/python/docs/_modules/mxnet/gluon/data/dataset.html
index 078a06e..97321a4 100644
--- a/api/python/docs/_modules/mxnet/gluon/data/dataset.html
+++ b/api/python/docs/_modules/mxnet/gluon/data/dataset.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/data/sampler.html b/api/python/docs/_modules/mxnet/gluon/data/sampler.html
index 2e36d99..639e123 100644
--- a/api/python/docs/_modules/mxnet/gluon/data/sampler.html
+++ b/api/python/docs/_modules/mxnet/gluon/data/sampler.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/data/vision/datasets.html b/api/python/docs/_modules/mxnet/gluon/data/vision/datasets.html
index fd9efef..41bb5c3 100644
--- a/api/python/docs/_modules/mxnet/gluon/data/vision/datasets.html
+++ b/api/python/docs/_modules/mxnet/gluon/data/vision/datasets.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/data/vision/transforms.html b/api/python/docs/_modules/mxnet/gluon/data/vision/transforms.html
index c25657d..a283431 100644
--- a/api/python/docs/_modules/mxnet/gluon/data/vision/transforms.html
+++ b/api/python/docs/_modules/mxnet/gluon/data/vision/transforms.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/loss.html b/api/python/docs/_modules/mxnet/gluon/loss.html
index 8164f14..57b7dbe 100644
--- a/api/python/docs/_modules/mxnet/gluon/loss.html
+++ b/api/python/docs/_modules/mxnet/gluon/loss.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/metric.html b/api/python/docs/_modules/mxnet/gluon/metric.html
index 7c1abb9..1cd3e91 100644
--- a/api/python/docs/_modules/mxnet/gluon/metric.html
+++ b/api/python/docs/_modules/mxnet/gluon/metric.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/model_zoo/vision.html b/api/python/docs/_modules/mxnet/gluon/model_zoo/vision.html
index 81392ff..38d8aed 100644
--- a/api/python/docs/_modules/mxnet/gluon/model_zoo/vision.html
+++ b/api/python/docs/_modules/mxnet/gluon/model_zoo/vision.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/alexnet.html b/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/alexnet.html
index a5fd379..2eb3bba 100644
--- a/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/alexnet.html
+++ b/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/alexnet.html
@@ -176,13 +176,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -751,13 +760,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/densenet.html b/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/densenet.html
index 7cad6e2..c2c2022 100644
--- a/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/densenet.html
+++ b/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/densenet.html
@@ -176,13 +176,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -751,13 +760,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/inception.html b/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/inception.html
index 6b97ab3..aa64b69 100644
--- a/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/inception.html
+++ b/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/inception.html
@@ -176,13 +176,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -751,13 +760,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/mobilenet.html b/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/mobilenet.html
index 69bb259..f416ff8 100644
--- a/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/mobilenet.html
+++ b/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/mobilenet.html
@@ -176,13 +176,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -751,13 +760,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/resnet.html b/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/resnet.html
index 32d5d1c..11fccdf 100644
--- a/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/resnet.html
+++ b/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/resnet.html
@@ -176,13 +176,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -751,13 +760,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/squeezenet.html b/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/squeezenet.html
index 167e7dc..58a4444 100644
--- a/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/squeezenet.html
+++ b/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/squeezenet.html
@@ -176,13 +176,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -751,13 +760,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/vgg.html b/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/vgg.html
index b1c404c..1dddefb 100644
--- a/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/vgg.html
+++ b/api/python/docs/_modules/mxnet/gluon/model_zoo/vision/vgg.html
@@ -176,13 +176,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -751,13 +760,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/nn/activations.html b/api/python/docs/_modules/mxnet/gluon/nn/activations.html
index 3494470..eac566d 100644
--- a/api/python/docs/_modules/mxnet/gluon/nn/activations.html
+++ b/api/python/docs/_modules/mxnet/gluon/nn/activations.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/nn/basic_layers.html b/api/python/docs/_modules/mxnet/gluon/nn/basic_layers.html
index de89855..f1b6b50 100644
--- a/api/python/docs/_modules/mxnet/gluon/nn/basic_layers.html
+++ b/api/python/docs/_modules/mxnet/gluon/nn/basic_layers.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/nn/conv_layers.html b/api/python/docs/_modules/mxnet/gluon/nn/conv_layers.html
index 61db186..45706e0 100644
--- a/api/python/docs/_modules/mxnet/gluon/nn/conv_layers.html
+++ b/api/python/docs/_modules/mxnet/gluon/nn/conv_layers.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/parameter.html b/api/python/docs/_modules/mxnet/gluon/parameter.html
index caf6578..482fb49 100644
--- a/api/python/docs/_modules/mxnet/gluon/parameter.html
+++ b/api/python/docs/_modules/mxnet/gluon/parameter.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -1720,10 +1738,13 @@ Edit on Github
<span class="n">ctx</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stype</span> <span class="o">==</span> <span class="s1">'default'</span><span class="p">:</span>
<span class="n">block</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">list_data</span><span class="p">()</span>
- <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span>
- <span class="n">data</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">w</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">block</span><span class="p">])</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span clas [...]
+ <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">block</span><span class="p">)</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
+ <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span>
+ <span class="n">data</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">w</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">block</span><span class="p">])</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span [...]
+ <span class="k">else</span><span class="p">:</span>
+ <span class="n">data</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">add_n</span><span class="p">(</span><span class="o">*</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">block</span><span [...]
<span class="k">else</span><span class="p">:</span>
- <span class="n">data</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">add_n</span><span class="p">(</span><span class="o">*</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">block</span><span clas [...]
+ <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">()</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># fetch all rows for 'row_sparse' param</span>
<span class="n">all_row_ids</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int64'</span><span class="p">, [...]
diff --git a/api/python/docs/_modules/mxnet/gluon/rnn/conv_rnn_cell.html b/api/python/docs/_modules/mxnet/gluon/rnn/conv_rnn_cell.html
index 4548bc4..314ed31 100644
--- a/api/python/docs/_modules/mxnet/gluon/rnn/conv_rnn_cell.html
+++ b/api/python/docs/_modules/mxnet/gluon/rnn/conv_rnn_cell.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/rnn/rnn_cell.html b/api/python/docs/_modules/mxnet/gluon/rnn/rnn_cell.html
index b5d9282..088f6f3 100644
--- a/api/python/docs/_modules/mxnet/gluon/rnn/rnn_cell.html
+++ b/api/python/docs/_modules/mxnet/gluon/rnn/rnn_cell.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/rnn/rnn_layer.html b/api/python/docs/_modules/mxnet/gluon/rnn/rnn_layer.html
index 9de1027..547eba6 100644
--- a/api/python/docs/_modules/mxnet/gluon/rnn/rnn_layer.html
+++ b/api/python/docs/_modules/mxnet/gluon/rnn/rnn_layer.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/trainer.html b/api/python/docs/_modules/mxnet/gluon/trainer.html
index 3a5b588..0a1a488 100644
--- a/api/python/docs/_modules/mxnet/gluon/trainer.html
+++ b/api/python/docs/_modules/mxnet/gluon/trainer.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/gluon/utils.html b/api/python/docs/_modules/mxnet/gluon/utils.html
index 0cd975b..b065854 100644
--- a/api/python/docs/_modules/mxnet/gluon/utils.html
+++ b/api/python/docs/_modules/mxnet/gluon/utils.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/image/detection.html b/api/python/docs/_modules/mxnet/image/detection.html
index eb5a4ee..ccac74e 100644
--- a/api/python/docs/_modules/mxnet/image/detection.html
+++ b/api/python/docs/_modules/mxnet/image/detection.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/image/image.html b/api/python/docs/_modules/mxnet/image/image.html
index 3a60de8..4f97667 100644
--- a/api/python/docs/_modules/mxnet/image/image.html
+++ b/api/python/docs/_modules/mxnet/image/image.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/initializer.html b/api/python/docs/_modules/mxnet/initializer.html
index 14e94fd..298cd9b 100644
--- a/api/python/docs/_modules/mxnet/initializer.html
+++ b/api/python/docs/_modules/mxnet/initializer.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/io/io.html b/api/python/docs/_modules/mxnet/io/io.html
index 2dc4ca2..d588a46 100644
--- a/api/python/docs/_modules/mxnet/io/io.html
+++ b/api/python/docs/_modules/mxnet/io/io.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/lr_scheduler.html b/api/python/docs/_modules/mxnet/lr_scheduler.html
index 843363a..b5c4bbb 100644
--- a/api/python/docs/_modules/mxnet/lr_scheduler.html
+++ b/api/python/docs/_modules/mxnet/lr_scheduler.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/ndarray/contrib.html b/api/python/docs/_modules/mxnet/ndarray/contrib.html
index 1538d97..6236718 100644
--- a/api/python/docs/_modules/mxnet/ndarray/contrib.html
+++ b/api/python/docs/_modules/mxnet/ndarray/contrib.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/ndarray/image.html b/api/python/docs/_modules/mxnet/ndarray/image.html
index 3ffa978..e0b4fc5 100644
--- a/api/python/docs/_modules/mxnet/ndarray/image.html
+++ b/api/python/docs/_modules/mxnet/ndarray/image.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/ndarray/linalg.html b/api/python/docs/_modules/mxnet/ndarray/linalg.html
index 26200f4..f71f9f0 100644
--- a/api/python/docs/_modules/mxnet/ndarray/linalg.html
+++ b/api/python/docs/_modules/mxnet/ndarray/linalg.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/ndarray/ndarray.html b/api/python/docs/_modules/mxnet/ndarray/ndarray.html
index ee3c308..caa482c 100644
--- a/api/python/docs/_modules/mxnet/ndarray/ndarray.html
+++ b/api/python/docs/_modules/mxnet/ndarray/ndarray.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/ndarray/random.html b/api/python/docs/_modules/mxnet/ndarray/random.html
index 5964adf..fe8c242 100644
--- a/api/python/docs/_modules/mxnet/ndarray/random.html
+++ b/api/python/docs/_modules/mxnet/ndarray/random.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/ndarray/sparse.html b/api/python/docs/_modules/mxnet/ndarray/sparse.html
index 25cc6f6..c617169 100644
--- a/api/python/docs/_modules/mxnet/ndarray/sparse.html
+++ b/api/python/docs/_modules/mxnet/ndarray/sparse.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/ndarray/utils.html b/api/python/docs/_modules/mxnet/ndarray/utils.html
index b3b37cd..35e03f6 100644
--- a/api/python/docs/_modules/mxnet/ndarray/utils.html
+++ b/api/python/docs/_modules/mxnet/ndarray/utils.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/adadelta.html b/api/python/docs/_modules/mxnet/optimizer/adadelta.html
index 6911880..72cd8f2 100644
--- a/api/python/docs/_modules/mxnet/optimizer/adadelta.html
+++ b/api/python/docs/_modules/mxnet/optimizer/adadelta.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/adagrad.html b/api/python/docs/_modules/mxnet/optimizer/adagrad.html
index 5e7818e..325216e 100644
--- a/api/python/docs/_modules/mxnet/optimizer/adagrad.html
+++ b/api/python/docs/_modules/mxnet/optimizer/adagrad.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/adam.html b/api/python/docs/_modules/mxnet/optimizer/adam.html
index 933271c..0f0d32a 100644
--- a/api/python/docs/_modules/mxnet/optimizer/adam.html
+++ b/api/python/docs/_modules/mxnet/optimizer/adam.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/adamax.html b/api/python/docs/_modules/mxnet/optimizer/adamax.html
index 7ccf972..e908161 100644
--- a/api/python/docs/_modules/mxnet/optimizer/adamax.html
+++ b/api/python/docs/_modules/mxnet/optimizer/adamax.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/dcasgd.html b/api/python/docs/_modules/mxnet/optimizer/dcasgd.html
index 5e5e4b4..65903dd 100644
--- a/api/python/docs/_modules/mxnet/optimizer/dcasgd.html
+++ b/api/python/docs/_modules/mxnet/optimizer/dcasgd.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/ftml.html b/api/python/docs/_modules/mxnet/optimizer/ftml.html
index 44e0505..1cee8c4 100644
--- a/api/python/docs/_modules/mxnet/optimizer/ftml.html
+++ b/api/python/docs/_modules/mxnet/optimizer/ftml.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/ftrl.html b/api/python/docs/_modules/mxnet/optimizer/ftrl.html
index 51de2d4..0c5495b 100644
--- a/api/python/docs/_modules/mxnet/optimizer/ftrl.html
+++ b/api/python/docs/_modules/mxnet/optimizer/ftrl.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/lamb.html b/api/python/docs/_modules/mxnet/optimizer/lamb.html
index 83049b5..885067d 100644
--- a/api/python/docs/_modules/mxnet/optimizer/lamb.html
+++ b/api/python/docs/_modules/mxnet/optimizer/lamb.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/lans.html b/api/python/docs/_modules/mxnet/optimizer/lans.html
index 6dd7195..475efd0 100644
--- a/api/python/docs/_modules/mxnet/optimizer/lans.html
+++ b/api/python/docs/_modules/mxnet/optimizer/lans.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/lars.html b/api/python/docs/_modules/mxnet/optimizer/lars.html
index dec2b84..8569187 100644
--- a/api/python/docs/_modules/mxnet/optimizer/lars.html
+++ b/api/python/docs/_modules/mxnet/optimizer/lars.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/nadam.html b/api/python/docs/_modules/mxnet/optimizer/nadam.html
index 2bd00a6..792db28 100644
--- a/api/python/docs/_modules/mxnet/optimizer/nadam.html
+++ b/api/python/docs/_modules/mxnet/optimizer/nadam.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/nag.html b/api/python/docs/_modules/mxnet/optimizer/nag.html
index d9e5f1c..2056905 100644
--- a/api/python/docs/_modules/mxnet/optimizer/nag.html
+++ b/api/python/docs/_modules/mxnet/optimizer/nag.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/optimizer.html b/api/python/docs/_modules/mxnet/optimizer/optimizer.html
index 468fa46..0738a93 100644
--- a/api/python/docs/_modules/mxnet/optimizer/optimizer.html
+++ b/api/python/docs/_modules/mxnet/optimizer/optimizer.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/rmsprop.html b/api/python/docs/_modules/mxnet/optimizer/rmsprop.html
index 91160f1..d8ef145 100644
--- a/api/python/docs/_modules/mxnet/optimizer/rmsprop.html
+++ b/api/python/docs/_modules/mxnet/optimizer/rmsprop.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/sgd.html b/api/python/docs/_modules/mxnet/optimizer/sgd.html
index e756a04..d926f9d 100644
--- a/api/python/docs/_modules/mxnet/optimizer/sgd.html
+++ b/api/python/docs/_modules/mxnet/optimizer/sgd.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/sgld.html b/api/python/docs/_modules/mxnet/optimizer/sgld.html
index f5bec9a..f565bca 100644
--- a/api/python/docs/_modules/mxnet/optimizer/sgld.html
+++ b/api/python/docs/_modules/mxnet/optimizer/sgld.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/signum.html b/api/python/docs/_modules/mxnet/optimizer/signum.html
index 2ede446..cea4d5d 100644
--- a/api/python/docs/_modules/mxnet/optimizer/signum.html
+++ b/api/python/docs/_modules/mxnet/optimizer/signum.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/optimizer/updater.html b/api/python/docs/_modules/mxnet/optimizer/updater.html
index 0c4ce79..48cf055 100644
--- a/api/python/docs/_modules/mxnet/optimizer/updater.html
+++ b/api/python/docs/_modules/mxnet/optimizer/updater.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/profiler.html b/api/python/docs/_modules/mxnet/profiler.html
index ca769dc..88f6820 100644
--- a/api/python/docs/_modules/mxnet/profiler.html
+++ b/api/python/docs/_modules/mxnet/profiler.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/recordio.html b/api/python/docs/_modules/mxnet/recordio.html
index 709679e..6efacba 100644
--- a/api/python/docs/_modules/mxnet/recordio.html
+++ b/api/python/docs/_modules/mxnet/recordio.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/rtc.html b/api/python/docs/_modules/mxnet/rtc.html
index 15c1411..fc49cf9 100644
--- a/api/python/docs/_modules/mxnet/rtc.html
+++ b/api/python/docs/_modules/mxnet/rtc.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/runtime.html b/api/python/docs/_modules/mxnet/runtime.html
index 7e5d070..bde8f42 100644
--- a/api/python/docs/_modules/mxnet/runtime.html
+++ b/api/python/docs/_modules/mxnet/runtime.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/symbol/contrib.html b/api/python/docs/_modules/mxnet/symbol/contrib.html
index a5a00af..fd81b52 100644
--- a/api/python/docs/_modules/mxnet/symbol/contrib.html
+++ b/api/python/docs/_modules/mxnet/symbol/contrib.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/symbol/image.html b/api/python/docs/_modules/mxnet/symbol/image.html
index 173ae09..8d76d74 100644
--- a/api/python/docs/_modules/mxnet/symbol/image.html
+++ b/api/python/docs/_modules/mxnet/symbol/image.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/symbol/linalg.html b/api/python/docs/_modules/mxnet/symbol/linalg.html
index 5293e28..a31f097 100644
--- a/api/python/docs/_modules/mxnet/symbol/linalg.html
+++ b/api/python/docs/_modules/mxnet/symbol/linalg.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/symbol/random.html b/api/python/docs/_modules/mxnet/symbol/random.html
index d7243c1..2aa40d2 100644
--- a/api/python/docs/_modules/mxnet/symbol/random.html
+++ b/api/python/docs/_modules/mxnet/symbol/random.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/symbol/sparse.html b/api/python/docs/_modules/mxnet/symbol/sparse.html
index d70c2a2..5be8319 100644
--- a/api/python/docs/_modules/mxnet/symbol/sparse.html
+++ b/api/python/docs/_modules/mxnet/symbol/sparse.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/symbol/symbol.html b/api/python/docs/_modules/mxnet/symbol/symbol.html
index 0c0e61d..1bcf5ad 100644
--- a/api/python/docs/_modules/mxnet/symbol/symbol.html
+++ b/api/python/docs/_modules/mxnet/symbol/symbol.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/test_utils.html b/api/python/docs/_modules/mxnet/test_utils.html
index abed9d4..6b7e1f1 100644
--- a/api/python/docs/_modules/mxnet/test_utils.html
+++ b/api/python/docs/_modules/mxnet/test_utils.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/util.html b/api/python/docs/_modules/mxnet/util.html
index 16071a2..4cfab49 100644
--- a/api/python/docs/_modules/mxnet/util.html
+++ b/api/python/docs/_modules/mxnet/util.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/mxnet/visualization.html b/api/python/docs/_modules/mxnet/visualization.html
index 518bb2a..f189352 100644
--- a/api/python/docs/_modules/mxnet/visualization.html
+++ b/api/python/docs/_modules/mxnet/visualization.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/numbers.html b/api/python/docs/_modules/numbers.html
index 28d67a9..c3d67b9 100644
--- a/api/python/docs/_modules/numbers.html
+++ b/api/python/docs/_modules/numbers.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_modules/symbol.html b/api/python/docs/_modules/symbol.html
index 15ca21c..36ff41c 100644
--- a/api/python/docs/_modules/symbol.html
+++ b/api/python/docs/_modules/symbol.html
@@ -175,13 +175,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
@@ -750,13 +759,22 @@ Edit on Github
<ul>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../tutorials/getting-started/index.html">Getting Started</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/crash-course/index.html">Getting started with NP on MXNet</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/1-ndarray.html">Step 1: Manipulate data with NP on MXNet</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/2-nn.html">Step 2: Create a neural network</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/4-train.html">Step 4: Train the neural network</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-predict.html">Step 5: Predict with a pretrained model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/6-use_gpus.html">Step 6: Use GPUs to increase efficiency</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/4-components.html">Necessary components that are not in the network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html"># Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-Datasets">## Introduction to <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Introduction-to-DataLoader">## Introduction to <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Machine-learning-with-Datasets-and-DataLoaders">## Machine learning with <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-Using-own-data-with-included-Datasets"># Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-Using-your-own-data-with-custom-Datasets"># Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html##-New-in-MXNet-2.0:-faster-C++-backend-dataloaders"># New in MXNet 2.0: faster C++ backend dataloaders</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/5-datasets.html###-Next-Steps">## Next Steps</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
diff --git a/api/python/docs/_sources/tutorials/getting-started/crash-course/0-introduction.ipynb b/api/python/docs/_sources/tutorials/getting-started/crash-course/0-introduction.ipynb
new file mode 100644
index 0000000..8f3b4fd0
--- /dev/null
+++ b/api/python/docs/_sources/tutorials/getting-started/crash-course/0-introduction.ipynb
@@ -0,0 +1,95 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<!--- Licensed to the Apache Software Foundation (ASF) under one -->\n",
+ "<!--- or more contributor license agreements. See the NOTICE file -->\n",
+ "<!--- distributed with this work for additional information -->\n",
+ "<!--- regarding copyright ownership. The ASF licenses this file -->\n",
+ "<!--- to you under the Apache License, Version 2.0 (the -->\n",
+ "<!--- \"License\"); you may not use this file except in compliance -->\n",
+ "<!--- with the License. You may obtain a copy of the License at -->\n",
+ "\n",
+ "<!--- http://www.apache.org/licenses/LICENSE-2.0 -->\n",
+ "\n",
+ "<!--- Unless required by applicable law or agreed to in writing, -->\n",
+ "<!--- software distributed under the License is distributed on an -->\n",
+ "<!--- \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->\n",
+ "<!--- KIND, either express or implied. See the License for the -->\n",
+ "<!--- specific language governing permissions and limitations -->\n",
+ "<!--- under the License. -->\n",
+ "\n",
+ "# Introduction\n",
+ "\n",
+ "\n",
+ "## About MXNet\n",
+ "\n",
+ "Apache MXNet is an open-source deep learning framework that provides a comprehensive and flexible API to create deep learning models. Some of the key features of MXNet are:\n",
+ "\n",
+ "1. **Fast and Scalable:** Easily supports multiple GPU's and distributed multi-host jobs. \n",
+ "2. **Multiple Programming language support:** Python, Scala, R, Java, C++, Julia, Matlab, JavaScript and Go interfaces. \n",
+ "3. **Supported:** Backed by Apache Software Foundation and supported by Amazon Web Services (AWS), Microsoft Azure and highly active open-source community.\n",
+ "4. **Portable:** Supports an efficient deployment on a wide range of hardware configurations and platforms i.e. low end devices, internet of things devices, serverless computing and containers.\n",
+ "5. **Flexible:** Supports both imperative and symbolic programming.\n",
+ "\n",
+ "\n",
+ "### Basic building blocks\n",
+ "\n",
+ "#### Tensors A.K.A Arrays\n",
+ "\n",
+ "Tensors give us a generic way of describing $n$-dimensional **arrays** with an arbitrary number of axes. Vectors, for example, are first-order tensors, and matrices are second-order tensors. Tensors with more than two orders(axes) do not have special mathematical names. The [ndarray](https://mxnet.apache.org/versions/1.7/api/python/docs/api/ndarray/index.html) package in MXNet provides a tensor implementation. This class is similar to NumPy's ndarray with additional features. First, [...]
+ "\n",
+ "You will get familiar to arrays in the [next section](1-nparray.md) of this crash course.\n",
+ "\n",
+ "### Computing paradigms\n",
+ "\n",
+ "#### Block\n",
+ "\n",
+ "Neural network designs like [ResNet-152](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf) have a fair degree of regularity. They consist of _blocks_ of repeated (or at least similarly designed) layers; these blocks then form the basis of more complex network designs. A block can be a single layer, a component consisting of multiple layers, or the entire complex neural network itself! One benefit of working with the bloc [...]
+ "\n",
+ "\n",
+ "From a programming standpoint, a block is represented by a class and [Block](https://mxnet.apache.org/versions/1.7/api/python/docs/api/gluon/nn/index.html#mxnet.gluon.nn.Block) is the base class for all neural networks layers in MXNet. Any subclass of it must define a forward propagation function that transforms its input into output and must store any necessary parameters if required.\n",
+ "\n",
+ "You will see more about blocks in [Array](1-nparray.md) and [Create neural network](2-create-nn.md) sections.\n",
+ "\n",
+ "#### HybridBlock\n",
+ "\n",
+ "Imperative and symbolic programming represents two styles or paradigms of deep learning programming interface and historically most deep learning frameworks choose either imperative or symbolic programming. For example, both Theano and TensorFlow (inspired by the latter) make use of symbolic programming, while Chainer and its predecessor PyTorch utilize imperative programming. \n",
+ "\n",
+ "The differences between imperative (interpreted) and symbolic programming are as follows:\n",
+ "\n",
+ "* __Imperative programming__ is easier. When imperative programming is used in Python, the majority of the code is straightforward and easy to write. It is also easier to debug imperative programming code. This is because it is easier to obtain and print all relevant intermediate variable values, or use Pythonʼs built-in debugging tools.\n",
+ " \n",
+ "* __Symbolic programming__ is more efficient and easier to port. It makes it easier to optimize the code during compilation, while also having the ability to port the program into a format independent of Python. This allows the program to be run in a non-Python environment, thus avoiding any potential performance issues related to the Python interpreter.\n",
+ "\n",
+ "You can learn more about the difference between symbolic vs. imperative programming from this [deep learning programming paradigm](https://mxnet.apache.org/versions/1.6/api/architecture/program_model) article\n",
+ "\n",
+ "When designing MXNet, developers considered whether it was possible to harness the benefits of both imperative and symbolic programming. The developers believed that users should be able to develop and debug using pure imperative programming, while having the ability to convert most programs into symbolic programming to be run when product-level computing performance and deployment are required. \n",
+ "\n",
+ "In hybrid programming, you can build models using either the [HybridBlock](https://mxnet.apache.org/versions/1.7/api/python/docs/api/gluon/hybrid_block.html) or the [HybridSequential](https://mxnet.apache.org/versions/1.6/api/python/docs/api/gluon/nn/index.html#mxnet.gluon.nn.HybridSequential) and [HybridConcurrent](https://mxnet.incubator.apache.org/versions/1.7/api/python/docs/api/gluon/contrib/index.html#mxnet.gluon.contrib.nn.HybridConcurrent) classes. By default, they are execu [...]
+ "\n",
+ "You will learn more about hybrid blocks and use them in the upcoming sections of the course.\n",
+ "\n",
+ "### Gluon\n",
+ "\n",
+ "Gluon is an imperative high-level front end API in MXNet for deep learning that’s flexible and easy-to-use which comes with a lot of great features, and it can provide you everything you need: from experimentation to deploying the model without sacrificing training speed. This is because, as discussed above, you have access to both imperative and symbolic APIs through the introduction of hybrid programming. Gluon provides State of the Art models for many of the standard tasks such a [...]
+ "\n",
+ "## Next steps\n",
+ "\n",
+ "Dive deeper on [array representations](1-nparray.md) in MXNet.\n",
+ "\n",
+ "## References\n",
+ "1. [Dive into Deep Learning](http://d2l.ai/)"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/api/python/docs/_sources/tutorials/getting-started/crash-course/1-ndarray.ipynb b/api/python/docs/_sources/tutorials/getting-started/crash-course/1-ndarray.ipynb
deleted file mode 100644
index a94136f..0000000
--- a/api/python/docs/_sources/tutorials/getting-started/crash-course/1-ndarray.ipynb
+++ /dev/null
@@ -1,357 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<!--- Licensed to the Apache Software Foundation (ASF) under one -->\n",
- "<!--- or more contributor license agreements. See the NOTICE file -->\n",
- "<!--- distributed with this work for additional information -->\n",
- "<!--- regarding copyright ownership. The ASF licenses this file -->\n",
- "<!--- to you under the Apache License, Version 2.0 (the -->\n",
- "<!--- \"License\"); you may not use this file except in compliance -->\n",
- "<!--- with the License. You may obtain a copy of the License at -->\n",
- "\n",
- "<!--- http://www.apache.org/licenses/LICENSE-2.0 -->\n",
- "\n",
- "<!--- Unless required by applicable law or agreed to in writing, -->\n",
- "<!--- software distributed under the License is distributed on an -->\n",
- "<!--- \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->\n",
- "<!--- KIND, either express or implied. See the License for the -->\n",
- "<!--- specific language governing permissions and limitations -->\n",
- "<!--- under the License. -->\n",
- "\n",
- "# Step 1: Manipulate data with NP on MXNet\n",
- "\n",
- "This getting started exercise introduces the `np` package, which is similar to Numpy. For more information, please see [Differences between NP on MXNet and NumPy](/api/python/docs/tutorials/getting-started/np/np-vs-numpy.html).\n",
- "\n",
- "## Import packages and create an array\n",
- "\n",
- "\n",
- "To get started, run the following commands to import the `np` package together with the NumPy extensions package `npx`. Together, `np` with `npx` make up the NP on MXNet front end."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "1"
- }
- },
- "outputs": [],
- "source": [
- "from mxnet import np, npx\n",
- "npx.set_np() # Activate NumPy-like mode."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In this step, create a 2D array (also called a matrix). The following code example creates a matrix with values from two sets of numbers: 1, 2, 3 and 4, 5, 6. This might also be referred to as a tuple of a tuple of integers."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "2"
- }
- },
- "outputs": [],
- "source": [
- "np.array(((1,2,3),(5,6,7)))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can also create a very simple matrix with the same shape (2 rows by 3 columns), but fill it with 1s."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "3"
- }
- },
- "outputs": [],
- "source": [
- "x = np.ones((2,3))\n",
- "x"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can create arrays whose values are sampled randomly. For example, sampling values uniformly between -1 and 1. The following code example creates the same shape, but with random sampling."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "15"
- }
- },
- "outputs": [],
- "source": [
- "y = np.random.uniform(-1,1, (2,3))\n",
- "y"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As with NumPy, the dimensions of each ndarray are shown by accessing the `.shape` attribute. As the following code example shows, you can also query for `size`, which is equal to the product of the components of the shape. In addition, `.dtype` tells the data type of the stored values."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "17"
- }
- },
- "outputs": [],
- "source": [
- "(x.shape, x.size, x.dtype)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Performing operations on an array\n",
- "\n",
- "An ndarray supports a large number of standard mathematical operations. Here are three examples. You can perform element-wise multiplication by using the following code example."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "18"
- }
- },
- "outputs": [],
- "source": [
- "x * y"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can perform exponentiation by using the following code example."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "23"
- }
- },
- "outputs": [],
- "source": [
- "np.exp(y)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can also find a matrix’s transpose to compute a proper matrix-matrix product by using the following code example."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "24"
- }
- },
- "outputs": [],
- "source": [
- "np.dot(x, y.T)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Indexing an array\n",
- "\n",
- "The ndarrays support slicing in many ways you might want to access your data. The following code example shows how to read a particular element, which returns a 1D array with shape `(1,)`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "25"
- }
- },
- "outputs": [],
- "source": [
- "y[1,2]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This example shows how to read the second and third columns from `y`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "26"
- }
- },
- "outputs": [],
- "source": [
- "y[:,1:3]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This example shows how to write to a specific element."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "27"
- }
- },
- "outputs": [],
- "source": [
- "y[:,1:3] = 2\n",
- "y"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can perform multi-dimensional slicing, which is shown in the following code example."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "28"
- }
- },
- "outputs": [],
- "source": [
- "y[1:2,0:2] = 4\n",
- "y"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Converting between MXNet ndarrays and NumPy ndarrays\n",
- "\n",
- "You can convert MXNet ndarrays to and from NumPy ndarrays, as shown in the following example. The converted arrays do not share memory."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "29"
- }
- },
- "outputs": [],
- "source": [
- "a = x.asnumpy()\n",
- "(type(a), a)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "30"
- }
- },
- "outputs": [],
- "source": [
- "np.array(a)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Next steps\n",
- "\n",
- "Learn how to construct a neural network with the Gluon module: [Step 2: Create a neural network](2-nn.md)."
- ]
- }
- ],
- "metadata": {
- "language_info": {
- "name": "python"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/api/python/docs/_sources/tutorials/getting-started/crash-course/1-nparray.ipynb b/api/python/docs/_sources/tutorials/getting-started/crash-course/1-nparray.ipynb
new file mode 100644
index 0000000..f07214f
--- /dev/null
+++ b/api/python/docs/_sources/tutorials/getting-started/crash-course/1-nparray.ipynb
@@ -0,0 +1,458 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<!--- Licensed to the Apache Software Foundation (ASF) under one -->\n",
+ "<!--- or more contributor license agreements. See the NOTICE file -->\n",
+ "<!--- distributed with this work for additional information -->\n",
+ "<!--- regarding copyright ownership. The ASF licenses this file -->\n",
+ "<!--- to you under the Apache License, Version 2.0 (the -->\n",
+ "<!--- \"License\"); you may not use this file except in compliance -->\n",
+ "<!--- with the License. You may obtain a copy of the License at -->\n",
+ "\n",
+ "<!--- http://www.apache.org/licenses/LICENSE-2.0 -->\n",
+ "\n",
+ "<!--- Unless required by applicable law or agreed to in writing, -->\n",
+ "<!--- software distributed under the License is distributed on an -->\n",
+ "<!--- \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->\n",
+ "<!--- KIND, either express or implied. See the License for the -->\n",
+ "<!--- specific language governing permissions and limitations -->\n",
+ "<!--- under the License. -->\n",
+ "\n",
+ "# Step 1: Manipulate data with NP on MXNet\n",
+ "\n",
+ "This getting started exercise introduces the MXNet `np` package for ndarrays.\n",
+ "These ndarrays extend the functionality of the common NumPy ndarrays, by adding\n",
+ "support for gpu's and by adding auto-differentiation with autograd. Now, many\n",
+ "NumPy methods are available within MXNet; therefore, we will only briefly cover\n",
+ "some of what is available.\n",
+ "\n",
+ "## Import packages and create an array\n",
+ "To get started, run the following commands to import the `np` package together\n",
+ "with the NumPy extensions package `npx`. Together, `np` with `npx` make up the\n",
+ "NP on MXNet front end."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "import mxnet as mx\n",
+ "from mxnet import np, npx\n",
+ "npx.set_np() # Activate NumPy-like mode.\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this step, create a 2D array (also called a matrix). The following code\n",
+ "example creates a matrix with values from two sets of numbers: 1, 2, 3 and 4, 5,\n",
+ "6. This might also be referred to as a tuple of a tuple of integers."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "np.array(((1, 2, 3), (5, 6, 7)))\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also create a very simple matrix with the same shape (2 rows by 3\n",
+ "columns), but fill it with 1's."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "x = np.full((2, 3), 1) \n",
+ "x\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Alternatively, you could use the following array creation routine."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "x = np.ones((2, 3)) \n",
+ "x\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can create arrays whose values are sampled randomly. For example, sampling\n",
+ "values uniformly between -1 and 1. The following code example creates the same\n",
+ "shape, but with random sampling."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "y = np.random.uniform(-1, 1, (2, 3))\n",
+ "y\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As with NumPy, the dimensions of each ndarray are shown by accessing the\n",
+ "`.shape` attribute. As the following code example shows, you can also query for\n",
+ "`size`, which is equal to the product of the components of the shape. In\n",
+ "addition, `.dtype` tells the data type of the stored values. As you notice when\n",
+ "we generate random uniform values we generate `float32` not `float64` as normal\n",
+ "NumPy arrays."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "(x.shape, x.size, x.dtype)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You could also specifiy the datatype when you create your ndarray."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "x = np.full((2, 3), 1, dtype=\"int8\") \n",
+ "x.dtype\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Versus the default of `float32`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "x = np.full((2, 3), 1) \n",
+ "x.dtype\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When we multiply, by default we use the datatype with the most precision."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "x = x.astype(\"int8\") + x.astype(int) + x.astype(\"float32\")\n",
+ "x.dtype\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Performing operations on an array\n",
+ "\n",
+ "A ndarray supports a large number of standard mathematical operations. Here are\n",
+ "some examples. You can perform element-wise multiplication by using the\n",
+ "following code example."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "x * y\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can perform exponentiation by using the following code example."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "np.exp(y)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also find a matrix’s transpose to compute a proper matrix-matrix product\n",
+ "by using the following code example."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "np.dot(x, y.T)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Alternatively, you could use the matrix multiplication function."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "np.matmul(x, y.T)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can leverage built in operators, like summation."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "x.sum()\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also gather a mean value."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "x.mean()\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can perform flatten and reshape just like you normally would in NumPy!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "x.flatten()\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "x.reshape(6, 1)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Indexing an array\n",
+ "\n",
+ "The ndarrays support slicing in many ways you might want to access your data.\n",
+ "The following code example shows how to read a particular element, which returns\n",
+ "a 1D array with shape `(1,)`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "y[1, 2]\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This example shows how to read the second and third columns from `y`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "y[:, 1:3]\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This example shows how to write to a specific element."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "y[:, 1:3] = 2\n",
+ "y\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can perform multi-dimensional slicing, which is shown in the following code\n",
+ "example."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "y[1:2, 0:2] = 4\n",
+ "y\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Converting between MXNet ndarrays and NumPy arrays\n",
+ "\n",
+ "You can convert MXNet ndarrays to and from NumPy ndarrays, as shown in the\n",
+ "following example. The converted arrays do not share memory."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "a = x.asnumpy()\n",
+ "(type(a), a)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "a = np.array(a)\n",
+ "(type(a), a)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Additionally, you can move them to different GPU contexts. You will dive more\n",
+ "into this later, but here is an example for now."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "a.copyto(mx.gpu(0))\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Next Steps\n",
+ "\n",
+ "Ndarrays also have some additional features which make Deep Learning possible\n",
+ "and efficient. Namely, differentiation, and being able to leverage GPU's.\n",
+ "Another important feature of ndarrays that we will discuss later is \n",
+ "autograd. But first, we will abstract an additional level and talk about building\n",
+ "Neural Network Layers [Step 2: Create a neural network](2-create-nn.md)"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/api/python/docs/_sources/tutorials/getting-started/crash-course/2-create-nn.ipynb b/api/python/docs/_sources/tutorials/getting-started/crash-course/2-create-nn.ipynb
new file mode 100644
index 0000000..62162d2
--- /dev/null
+++ b/api/python/docs/_sources/tutorials/getting-started/crash-course/2-create-nn.ipynb
@@ -0,0 +1,549 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<!--- Licensed to the Apache Software Foundation (ASF) under one --> \n",
+ "<!--- or more contributor license agreements. See the NOTICE file --> \n",
+ "<!--- distributed with this work for additional information --> \n",
+ "<!--- regarding copyright ownership. The ASF licenses this file --> \n",
+ "<!--- to you under the Apache License, Version 2.0 (the --> \n",
+ "<!--- \"License\"); you may not use this file except in compliance --> \n",
+ "<!--- with the License. You may obtain a copy of the License at --> \n",
+ " \n",
+ "<!--- http://www.apache.org/licenses/LICENSE-2.0 --> \n",
+ " \n",
+ "<!--- Unless required by applicable law or agreed to in writing, --> \n",
+ "<!--- software distributed under the License is distributed on an --> \n",
+ "<!--- \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --> \n",
+ "<!--- KIND, either express or implied. See the License for the --> \n",
+ "<!--- specific language governing permissions and limitations --> \n",
+ "<!--- under the License. -->\n",
+ "# Step 2: Create a neural network \n",
+ " \n",
+ "In this step, you learn how to use NP on Apache MXNet to create neural networks \n",
+ "in Gluon. In addition to the `np` package that you learned about in the previous \n",
+ "step [Step 1: Manipulate data with NP on MXNet](1-nparray.md), you also need to \n",
+ "import the neural network modules from `gluon`. Gluon includes built-in neural \n",
+ "network layers in the following two modules: \n",
+ " \n",
+ "1. `mxnet.gluon.nn`: NN module that maintained by the mxnet team \n",
+ "2. `mxnet.gluon.contrib.nn`: Experiemental module that is contributed by the \n",
+ "community \n",
+ " \n",
+ "Use the following commands to import the packages required for this step. \n",
+ " \n",
+ "```python \n",
+ "from mxnet import np, npx \n",
+ "from mxnet.gluon import nn \n",
+ "npx.set_np() # Change MXNet to the numpy-like mode. \n",
+ "``` \n",
+ " \n",
+ "## Create your neural network's first layer \n",
+ " \n",
+ "In this section, you will create a simple neural network with Gluon. One of the \n",
+ "simplest network you can create is a single **Dense** layer or **densely- \n",
+ "connected** layer. A dense layer consists of nodes in the input that are \n",
+ "connected to every node in the next layer. Use the following code example to \n",
+ "start with a dense layer with five output units. \n",
+ " \n",
+ "```python \n",
+ "layer = nn.Dense(5) \n",
+ "layer \n",
+ "# output: Dense(-1 -> 5, linear) \n",
+ "``` \n",
+ " \n",
+ "In the example above, the output is `Dense(-1 -> 5, linear)`. The **-1** in the \n",
+ "output denotes that the size of the input layer is not specified during \n",
+ "initialization. \n",
+ " \n",
+ "You can also call the **Dense** layer with an `in_units` parameter if you know \n",
+ "the shape of your input unit. \n",
+ " \n",
+ "```python \n",
+ "layer = nn.Dense(5,in_units=3) \n",
+ "layer \n",
+ "``` \n",
+ " \n",
+ "In addition to the `in_units` param, you can also add an activation function to \n",
+ "the layer using the `activation` param. The Dense layer implements the operation \n",
+ " \n",
+ "$$output = \\sigma(W \\cdot X + b)$$ \n",
+ " \n",
+ "Call the Dense layer with an `activation` parameter to use an activation \n",
+ "function. \n",
+ " \n",
+ "```python \n",
+ "layer = nn.Dense(5, in_units=3,activation='relu') \n",
+ "``` \n",
+ " \n",
+ "Voila! Congratulations on creating a simple neural network. But for most of your \n",
+ "use cases, you will need to create a neural network with more than one dense \n",
+ "layer or with multiple types of other layers. In addition to the `Dense` layer, \n",
+ "you can find more layers at [mxnet nn \n",
+ "layers](https://mxnet.apache.org/versions/1.6/api/python/docs/api/gluon/nn/index.html#module- \n",
+ "mxnet.gluon.nn) \n",
+ " \n",
+ "So now that you have created a neural network, you are probably wondering how to \n",
+ "pass data into your network? \n",
+ " \n",
+ "First, you need to initialize the network weights, if you use the default \n",
+ "initialization method which draws random values uniformly in the range $[-0.7, \n",
+ "0.7]$. You can see this in the following example. \n",
+ " \n",
+ "**Note**: Initialization is discussed at a little deeper detail in the next \n",
+ "notebook \n",
+ " \n",
+ "```python \n",
+ "layer.initialize() \n",
+ "``` \n",
+ " \n",
+ "Now that you have initialized your network, you can give it data. Passing data \n",
+ "through a network is also called a forward pass. You can do a forward pass with \n",
+ "random data, shown in the following example. First, you create a `(10,3)` shape \n",
+ "random input `x` and feed the data into the layer to compute the output. \n",
+ " \n",
+ "```python \n",
+ "x = np.random.uniform(-1,1,(10,3)) \n",
+ "layer(x) \n",
+ "``` \n",
+ " \n",
+ "The layer produces a `(10,5)` shape output from your `(10,3)` input. \n",
+ " \n",
+ "**When you don't specify the `in_unit` parameter, the system automatically \n",
+ "infers it during the first time you feed in data during the first forward step \n",
+ "after you create and initialize the weights.** \n",
+ " \n",
+ " \n",
+ "```python \n",
+ "layer.params \n",
+ "``` \n",
+ " \n",
+ "The `weights` and `bias` can be accessed using the `.data()` method. \n",
+ " \n",
+ "```python \n",
+ "layer.weight.data() \n",
+ "``` \n",
+ " \n",
+ "## Chain layers into a neural network using nn.Sequential \n",
+ " \n",
+ "Sequential provides a special way of rapidly building networks when when the \n",
+ "network architecture follows a common design pattern: the layers look like a \n",
+ "stack of pancakes. Many networks follow this pattern: a bunch of layers, one \n",
+ "stacked on top of another, where the output of each layer is fed directly to the \n",
+ "input to the next layer. To use sequential, simply provide a list of layers \n",
+ "(pass in the layers by calling `net.add(<Layer goes here!>`). To do this you can \n",
+ "use your previous example of Dense layers and create a 3-layer multi layer \n",
+ "perceptron. You can create a sequential block using `nn.Sequential()` method and \n",
+ "add layers using `add()` method. \n",
+ " \n",
+ "```python \n",
+ "net = nn.Sequential() \n",
+ " \n",
+ "net.add(nn.Dense(5,in_units=3,activation='relu'), \n",
+ " nn.Dense(25, activation='relu'), nn.Dense(2) ) \n",
+ "net \n",
+ "``` \n",
+ " \n",
+ "The layers are ordered exactly the way you defined your neural network with \n",
+ "index starting from 0. You can access the layers by indexing the network using \n",
+ "`[]`. \n",
+ " \n",
+ "```python \n",
+ "net[1] \n",
+ "``` \n",
+ " \n",
+ "## Create a custom neural network architecture flexibly \n",
+ " \n",
+ "`nn.Sequential()` allows you to create your multi-layer neural network with \n",
+ "existing layers from `gluon.nn`. It also includes a pre-defined `forward()` \n",
+ "function that sequentially executes added layers. But what if the built-in \n",
+ "layers are not sufficient for your needs. If you want to create networks like \n",
+ "ResNet which has complex but repeatable components, how do you create such a \n",
+ "network? \n",
+ " \n",
+ "In gluon, every neural network layer is defined by using a base class \n",
+ "`nn.Block()`. A Block has one main job - define a forward method that takes some \n",
+ "input x and generates an output. A Block can just do something simple like apply \n",
+ "an activation function. It can combine multiple layers together in a single \n",
+ "block or also combine a bunch of other Blocks together in creative ways to \n",
+ "create complex networks like Resnet. In this case, you will construct three \n",
+ "Dense layers. The `forward()` method can then invoke the layers in turn to \n",
+ "generate its output. \n",
+ " \n",
+ "Create a subclass of `nn.Block` and implement two methods by using the following \n",
+ "code. \n",
+ " \n",
+ "- `__init__` create the layers \n",
+ "- `forward` define the forward function. \n",
+ " \n",
+ "``` \n",
+ "class Net(nn.Block): \n",
+ " def __init__(self): super().__init__() \n",
+ " def forward(self, x): return x``` \n",
+ " \n",
+ "```python \n",
+ "class MLP(nn.Block): \n",
+ " def __init__(self): super().__init__() self.dense1 = nn.Dense(5,activation='relu') self.dense2 = nn.Dense(25,activation='relu') self.dense3 = nn.Dense(2) \n",
+ " def forward(self, x): layer1 = self.dense1(x) layer2 = self.dense2(layer1) layer3 = self.dense3(layer2) return layer3 net = MLP() \n",
+ "net \n",
+ "``` \n",
+ " \n",
+ "```python \n",
+ "net.dense1.params \n",
+ "``` \n",
+ "Each layer includes parameters that are stored in a `Parameter` class. You can \n",
+ "access them using the `params()` method. \n",
+ " \n",
+ "## Creating custom layers using Parameters (Blocks API) \n",
+ " \n",
+ "MXNet includes a `Parameter` method to hold your parameters in each layer. You \n",
+ "can create custom layers using the `Parameter` class to include computation that \n",
+ "may otherwise be not included in the built-in layers. For example, for a dense \n",
+ "layer, the weights and biases will be created using the `Parameter` method. But \n",
+ "if you want to add additional computation to the dense layer, you can create it \n",
+ "using parameter method. \n",
+ " \n",
+ "Instantiate a parameter, e.g weights with a size `(5,0)` using the `shape` \n",
+ "argument. \n",
+ " \n",
+ "```python \n",
+ "from mxnet.gluon import Parameter \n",
+ " \n",
+ "weight = Parameter(\"custom_parameter_weight\",shape=(5,-1)) \n",
+ "bias = Parameter(\"custom_parameter_bias\",shape=(5,-1)) \n",
+ " \n",
+ "weight,bias \n",
+ "``` \n",
+ " \n",
+ "The `Parameter` method includes a `grad_req` argument that specifies how you \n",
+ "want to capture gradients for this Parameter. Under the hood, that lets gluon \n",
+ "know that it has to call `.attach_grad()` on the underlying array. By default, \n",
+ "the gradient is updated everytime the gradient is written to the grad \n",
+ "`grad_req='write'`. \n",
+ " \n",
+ "Now that you know how parameters work, you are ready to create your very own \n",
+ "fully-connected custom layer. \n",
+ " \n",
+ "To create the custom layers using parameters, you can use the same skeleton with \n",
+ "`nn.Block` base class. You will create a custom dense layer that takes parameter \n",
+ "x and returns computed `w*x + b` without any activation function \n",
+ " \n",
+ "```python \n",
+ "class custom_layer(nn.Block): \n",
+ " def __init__(self,out_units,in_units=0): super().__init__() self.weight = Parameter(\"weight\",shape=(in_units,out_units),allow_deferred_init=True) self.bias = Parameter(\"bias\",shape=(out_units,),allow_deferred_init=True) \n",
+ " def forward(self, x): return np.dot(x, self.weight.data()) + self.bias.data()``` \n",
+ " \n",
+ "Parameter can be instantiated before the corresponding data is instantiated. For \n",
+ "example, when you instantiate a Block but the shapes of each parameter still \n",
+ "need to be inferred, the Parameter will wait for the shape to be inferred before \n",
+ "allocating memory. \n",
+ " \n",
+ "```python \n",
+ "dense = custom_layer(3,in_units=5) \n",
+ "dense.initialize() \n",
+ "dense(np.random.uniform(size=(4, 5))) \n",
+ "``` \n",
+ " \n",
+ "Similarly, you can use the following code to implement a famous network called \n",
+ "[LeNet](http://yann.lecun.com/exdb/lenet/) through `nn.Block` using the built-in \n",
+ "`Dense` layer and using `custom_layer` as the last layer \n",
+ " \n",
+ "```python \n",
+ "class LeNet(nn.Block): \n",
+ " def __init__(self): super().__init__() self.conv1 = nn.Conv2D(channels=6, kernel_size=3, activation='relu') self.pool1 = nn.MaxPool2D(pool_size=2, strides=2) self.conv2 = nn.Conv2D(channels=16, kernel_size=3, activation='relu') self.pool2 = nn.MaxPool2D(pool_size=2, strides=2) self.dense1 = nn.Dense(120, activation=\"relu\") self.dense2 = nn.Dense(84, activation=\"relu\") self.dense3 = nn.Dense(10) \n",
+ " def forward(self, x): x = self.conv1(x) x = self.pool1(x) x = self.conv2(x) x = self.pool2(x) x = self.dense1(x) x = self.dense2(x) x = self.dense3(x) return x Lenet = LeNet() \n",
+ "``` \n",
+ " \n",
+ "```python \n",
+ "class LeNet_custom(nn.Block): \n",
+ " def __init__(self): super().__init__() self.conv1 = nn.Conv2D(channels=6, kernel_size=3, activation='relu') self.pool1 = nn.MaxPool2D(pool_size=2, strides=2) self.conv2 = nn.Conv2D(channels=16, kernel_size=3, activation='relu') self.pool2 = nn.MaxPool2D(pool_size=2, strides=2) self.dense1 = nn.Dense(120, activation=\"relu\") self.dense2 = nn.Dense(84, activation=\"relu\") self.dense3 = custom_layer(10,84) \n",
+ " def forward(self, x): x = self.conv1(x) x = self.pool1(x) x = self.conv2(x) x = self.pool2(x) x = self.dense1(x) x = self.dense2(x) x = self.dense3(x) return x Lenet_custom = LeNet_custom() \n",
+ "``` \n",
+ " \n",
+ "```python \n",
+ "image_data = np.random.uniform(-1,1, (1,1,28,28)) \n",
+ " \n",
+ "Lenet.initialize() \n",
+ "Lenet_custom.initialize() \n",
+ " \n",
+ "print(\"Lenet:\") \n",
+ "print(Lenet(image_data)) \n",
+ " \n",
+ "print(\"Custom Lenet:\") \n",
+ "print(Lenet_custom(image_data)) \n",
+ "``` \n",
+ " \n",
+ " \n",
+ "You can use `.data` method to access the weights and bias of a particular layer. \n",
+ "For example, the following accesses the first layer's weight and sixth layer's bias. \n",
+ " \n",
+ "```python \n",
+ "Lenet.conv1.weight.data().shape, Lenet.dense1.bias.data().shape \n",
+ "``` \n",
+ " \n",
+ "## Using predefined (pretrained) architectures \n",
+ " \n",
+ "Till now, you have seen how to create your own neural network architectures. But \n",
+ "what if you want to replicate or baseline your dataset using some of the common \n",
+ "models in computer visions or natural language processing (NLP). Gluon includes \n",
+ "common architectures that you can directly use. The Gluon Model Zoo provides a \n",
+ "collection of off-the-shelf models e.g. RESNET, BERT etc. These architectures \n",
+ "are found at: \n",
+ " \n",
+ "- [Gluon CV model zoo](https://gluon-cv.mxnet.io/model_zoo/index.html) \n",
+ " \n",
+ "- [Gluon NLP model zoo](https://gluon-nlp.mxnet.io/model_zoo/index.html) \n",
+ " \n",
+ "```python \n",
+ "from mxnet.gluon import model_zoo \n",
+ " \n",
+ "net = model_zoo.vision.resnet50_v2(pretrained=True) \n",
+ "net.hybridize() \n",
+ " \n",
+ "dummy_input = np.ones(shape=(1,3,224,224)) \n",
+ "output = net(dummy_input) \n",
+ "output.shape \n",
+ "``` \n",
+ " \n",
+ "## Deciding the paradigm for your network \n",
+ " \n",
+ "In MXNet, Gluon API (Imperative programming paradigm) provides a user friendly \n",
+ "way for quick prototyping, easy debugging and natural control flow for people \n",
+ "familiar with python programming. \n",
+ " \n",
+ "However, at the backend, MXNET can also convert the network using Symbolic or \n",
+ "Declarative programming into static graphs with low level optimizations on \n",
+ "operators. However, static graphs are less flexible because any logic must be \n",
+ "encoded into the graph as special operators like scan, while_loop and cond. It’s \n",
+ "also hard to debug. \n",
+ " \n",
+ "So how can you make use of symbolic programming while getting the flexibility of \n",
+ "imperative programming to quickly prototype and debug? \n",
+ " \n",
+ "Enter **HybridBlock** \n",
+ " \n",
+ "HybridBlocks can run in a fully imperatively way where you define their \n",
+ "computation with real functions acting on real inputs. But they’re also capable \n",
+ "of running symbolically, acting on placeholders. Gluon hides most of this under \n",
+ "the hood so you will only need to know how it works when you want to write your \n",
+ "own layers. \n",
+ " \n",
+ "```python \n",
+ "net_hybrid_seq = nn.HybridSequential() \n",
+ " \n",
+ "net_hybrid_seq.add(nn.Dense(5,in_units=3,activation='relu'), \n",
+ " nn.Dense(25, activation='relu'), nn.Dense(2) ) \n",
+ "net_hybrid_seq \n",
+ "``` \n",
+ " \n",
+ "To compile and optimize `HybridSequential`, you can call its `hybridize` method. \n",
+ " \n",
+ "```python \n",
+ "net_hybrid_seq.hybridize() \n",
+ "``` \n",
+ "\n",
+ " \n",
+ "## Creating custom layers using Parameters (HybridBlocks API) \n",
+ " \n",
+ "When you instantiated your custom layer, you specified the input dimension \n",
+ "`in_units` that initializes the weights with the shape specified by `in_units` \n",
+ "and `out_units`. If you leave the shape of `in_unit` as unknown, you defer the \n",
+ "shape to the first forward pass. For the custom layer, you define the \n",
+ "`infer_shape()` method and let the shape be inferred at runtime. \n",
+ " \n",
+ "```python \n",
+ "class custom_layer(nn.HybridBlock): \n",
+ " def __init__(self,out_units,in_units=-1): super().__init__() self.weight = Parameter(\"weight\",shape=(in_units,out_units),allow_deferred_init=True) self.bias = Parameter(\"bias\",shape=(out_units,),allow_deferred_init=True) def forward(self, x): \n",
+ " print(self.weight.shape,self.bias.shape) return np.dot(x, self.weight.data()) + self.bias.data() def infer_shape(self, x): \n",
+ " print(self.weight.shape,x.shape) self.weight.shape = (x.shape[-1],self.weight.shape[1]) dense = custom_layer(3) \n",
+ "dense.initialize() \n",
+ "dense(np.random.uniform(size=(4, 5))) \n",
+ "``` \n",
+ " \n",
+ "### Performance \n",
+ " \n",
+ "To get a sense of the speedup from hybridizing, you can compare the performance \n",
+ "before and after hybridizing by measuring the time it takes to make 1000 forward \n",
+ "passes through the network. \n",
+ " \n",
+ "```python \n",
+ "from time import time \n",
+ " \n",
+ "def benchmark(net, x): \n",
+ " y = net(x) start = time() for i in range(1,1000): y = net(x) return time() - start \n",
+ "x_bench = np.random.normal(size=(1,512)) \n",
+ " \n",
+ "net_hybrid_seq = nn.HybridSequential() \n",
+ " \n",
+ "net_hybrid_seq.add(nn.Dense(256,activation='relu'), \n",
+ " nn.Dense(128, activation='relu'), nn.Dense(2) )net_hybrid_seq.initialize() \n",
+ " \n",
+ "print('Before hybridizing: %.4f sec'%(benchmark(net_hybrid_seq, x_bench))) \n",
+ "net_hybrid_seq.hybridize() \n",
+ "print('After hybridizing: %.4f sec'%(benchmark(net_hybrid_seq, x_bench))) \n",
+ "``` \n",
+ " \n",
+ "Peeling back another layer, you also have a `HybridBlock` which is the hybrid \n",
+ "version of the `Block` API. \n",
+ " \n",
+ "Similar to the `Blocks` API, you define a `forward` function for `HybridBlock` \n",
+ "that takes an input `x`. MXNet takes care of hybridizing the model at the \n",
+ "backend so you don't have to make changes to your code to convert it to a \n",
+ "symbolic paradigm. \n",
+ " \n",
+ "```python \n",
+ "from mxnet.gluon import HybridBlock \n",
+ " \n",
+ "class MLP_Hybrid(HybridBlock): \n",
+ " def __init__(self): super().__init__() self.dense1 = nn.Dense(256,activation='relu') self.dense2 = nn.Dense(128,activation='relu') self.dense3 = nn.Dense(2) \n",
+ " def forward(self, x): layer1 = self.dense1(x) layer2 = self.dense2(layer1) layer3 = self.dense3(layer2) return layer3 net_Hybrid = MLP_Hybrid() \n",
+ "net_Hybrid.initialize() \n",
+ " \n",
+ "print('Before hybridizing: %.4f sec'%(benchmark(net_Hybrid, x_bench))) \n",
+ "net_Hybrid.hybridize() \n",
+ "print('After hybridizing: %.4f sec'%(benchmark(net_Hybrid, x_bench))) \n",
+ "``` \n",
+ " \n",
+ "Given a HybridBlock whose forward computation consists of going through other \n",
+ "HybridBlocks, you can compile that section of the network by calling the \n",
+ "HybridBlocks `.hybridize()` method. \n",
+ " \n",
+ "All of MXNet’s predefined layers are HybridBlocks. This means that any network \n",
+ "consisting entirely of predefined MXNet layers can be compiled and run at much \n",
+ "faster speeds by calling `.hybridize()`. \n",
+ " \n",
+ "## Saving and Loading your models \n",
+ " \n",
+ "The Blocks API also includes saving your models during and after training so \n",
+ "that you can host the model for inference or avoid training the model again from \n",
+ "scratch. Another reason would be to train your model using one language (like \n",
+ "Python that has a lot of tools for training) and run inference using a different \n",
+ "language. \n",
+ " \n",
+ "There are two ways to save your model in MXNet. \n",
+ "1. Save/load the model weights/parameters only \n",
+ "2. Save/load the model weights/parameters and the architectures \n",
+ " \n",
+ "### 1. Save/load the model weights/parameters only\n",
+ " \n",
+ "You can use `save_parameters` and `load_parameters` method to save and load the \n",
+ "model weights. Take your simplest model `layer` and save your parameters first. \n",
+ "The model parameters are the params that you save **after** you train your \n",
+ "model. \n",
+ " \n",
+ "```python \n",
+ "file_name = 'layer.params' \n",
+ "layer.save_parameters(file_name) \n",
+ "``` \n",
+ " \n",
+ "And now load this model again. To load the parameters into a model, you will \n",
+ "first have to build the model. To do this, you will need to create a simple \n",
+ "function to build it. \n",
+ " \n",
+ "```python \n",
+ "def build_model(): \n",
+ " layer = nn.Dense(5, in_units=3,activation='relu') return layer \n",
+ "layer_new = build_model() \n",
+ "``` \n",
+ " \n",
+ "```python \n",
+ "layer_new.load_parameters('layer.params') \n",
+ "``` \n",
+ " \n",
+ "**Note**: The `save_parameters` and `load_parameters` method is used for models \n",
+ "that use a `Block` method instead of `HybridBlock` method to build the model. \n",
+ "These models may have complex architectures where the model architectures may \n",
+ "change during execution. E.g. if you have a model that uses an if-else \n",
+ "conditional statement to choose between two different architectures. \n",
+ " \n",
+ "### 2. Save/load the model weights/parameters and the architectures\n",
+ " \n",
+ "For models that use the **HybridBlock**, the model architecture stays static and \n",
+ "do no change during execution. Therefore both model parameters **AND** \n",
+ "architecture can be saved and loaded using `export`, `imports` methods. \n",
+ " \n",
+ "Now look at your `MLP_Hybrid` model and export the model using the `export` \n",
+ "function. The export function will export the model architecture into a `.json` \n",
+ "file and model parameters into a `.params` file. \n",
+ " \n",
+ "```python \n",
+ "net_Hybrid.export('MLP_hybrid') \n",
+ "``` \n",
+ " \n",
+ "```python \n",
+ "net_Hybrid.export('MLP_hybrid') \n",
+ "``` \n",
+ " \n",
+ "Similarly, to load this model back, you can use `gluon.nn.SymbolBlock`. To \n",
+ "demonstrate that, load the network serialized above. \n",
+ " \n",
+ "```python \n",
+ "import warnings \n",
+ "with warnings.catch_warnings(): \n",
+ " warnings.simplefilter(\"ignore\") net_loaded = nn.SymbolBlock.imports(\"MLP_hybrid-symbol.json\", ['data'], \"MLP_hybrid-0000.params\",ctx=None)``` \n",
+ " \n",
+ "```python \n",
+ "net_loaded(x_bench) \n",
+ "``` \n",
+ " \n",
+ "## Visualizing your models \n",
+ " \n",
+ "In MXNet, the `Block.Summary()` method allows you to view the block’s shape \n",
+ "arguments and view the block’s parameters. When you combine multiple blocks into \n",
+ "a model, the `summary()` applied on the model allows you to view each block’s \n",
+ "summary, the total parameters, and the order of the blocks within the model. To \n",
+ "do this the `Block.summary()` method requires one forward pass of the data, \n",
+ "through your network, in order to create the graph necessary for capturing the \n",
+ "corresponding shapes and parameters. Additionally, this method should be called \n",
+ "before the hybridize method, since the hybridize method converts the graph into \n",
+ "a symbolic one, potentially changing the operations for optimal computation. \n",
+ " \n",
+ "Look at the following examples \n",
+ " \n",
+ "- layer: our single layer network \n",
+ "- Lenet: a non-hybridized LeNet network \n",
+ "- net_Hybrid: our MLP Hybrid network \n",
+ " \n",
+ "```python \n",
+ "layer.summary(x) \n",
+ "``` \n",
+ " \n",
+ "```python \n",
+ "Lenet.summary(image_data) \n",
+ "``` \n",
+ " \n",
+ "You are able to print the summaries of the two networks `layer` and `Lenet` \n",
+ "easily since you didn't hybridize the two networks. However, the last network \n",
+ "`net_Hybrid` was hybridized above and throws an `AssertionError` if you try \n",
+ "`net_Hybrid.summary(x_bench)`. To print the summary for `net_Hybrid`, call \n",
+ "another instance of the same network and instantiate it for our summary and then \n",
+ "hybridize it \n",
+ " \n",
+ "```python \n",
+ "net_Hybrid_summary = MLP_Hybrid() \n",
+ " \n",
+ "net_Hybrid_summary.initialize() \n",
+ " \n",
+ "net_Hybrid_summary.summary(x_bench) \n",
+ " \n",
+ "net_Hybrid_summary.hybridize() \n",
+ "``` \n",
+ " \n",
+ "## Next steps: \n",
+ " \n",
+ "Now that you have created a neural network, learn how to automatically compute \n",
+ "the gradients in [Step 3: Automatic differentiation with \n",
+ "autograd](3-autograd.md)."
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/api/python/docs/_sources/tutorials/getting-started/crash-course/2-nn.ipynb b/api/python/docs/_sources/tutorials/getting-started/crash-course/2-nn.ipynb
deleted file mode 100644
index d8c8fcb..0000000
--- a/api/python/docs/_sources/tutorials/getting-started/crash-course/2-nn.ipynb
+++ /dev/null
@@ -1,319 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<!--- Licensed to the Apache Software Foundation (ASF) under one -->\n",
- "<!--- or more contributor license agreements. See the NOTICE file -->\n",
- "<!--- distributed with this work for additional information -->\n",
- "<!--- regarding copyright ownership. The ASF licenses this file -->\n",
- "<!--- to you under the Apache License, Version 2.0 (the -->\n",
- "<!--- \"License\"); you may not use this file except in compliance -->\n",
- "<!--- with the License. You may obtain a copy of the License at -->\n",
- "\n",
- "<!--- http://www.apache.org/licenses/LICENSE-2.0 -->\n",
- "\n",
- "<!--- Unless required by applicable law or agreed to in writing, -->\n",
- "<!--- software distributed under the License is distributed on an -->\n",
- "<!--- \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->\n",
- "<!--- KIND, either express or implied. See the License for the -->\n",
- "<!--- specific language governing permissions and limitations -->\n",
- "<!--- under the License. -->\n",
- "\n",
- "# Step 2: Create a neural network\n",
- "\n",
- "In this step, you learn how to use NP on MXNet to create neural networks in Gluon. In addition to the `np` package that you learned about in the previous step [Step 1: Manipulate data with NP on MXNet](1-ndarray.md), you also import the neural network `nn` package from `gluon`.\n",
- "\n",
- "Use the following commands to import the packages required for this step."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "2"
- }
- },
- "outputs": [],
- "source": [
- "from mxnet import np, npx\n",
- "from mxnet.gluon import nn\n",
- "npx.set_np() # Change MXNet to the numpy-like mode."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Create your neural network's first layer\n",
- "\n",
- "Use the following code example to start with a dense layer with two output units.\n",
- "<!-- mention what the none and the linear parts mean? -->"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "31"
- }
- },
- "outputs": [],
- "source": [
- "layer = nn.Dense(2)\n",
- "layer"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Initialize its weights with the default initialization method, which draws random values uniformly from $[-0.7, 0.7]$. You can see this in the following example."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "32"
- }
- },
- "outputs": [],
- "source": [
- "layer.initialize()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Do a forward pass with random data, shown in the following example. We create a $(3,4)$ shape random input `x` and feed into the layer to compute the output."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "34"
- }
- },
- "outputs": [],
- "source": [
- "x = np.random.uniform(-1,1,(3,4))\n",
- "layer(x)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As can be seen, the layer's input limit of two produced a $(3,2)$ shape output from our $(3,4)$ input. You didn't specify the input size of `layer` before, though you can specify it with the argument `in_units=4` here. The system automatically infers it during the first time you feed in data, create, and initialize the weights. You can access the weight after the first forward pass, as shown in this example."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "35"
- }
- },
- "outputs": [],
- "source": [
- "layer.weight.data()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Chain layers into a neural network\n",
- "\n",
- "Consider a simple case where a neural network is a chain of layers. During the forward pass, you run layers sequentially one-by-one. Use the following code to implement a famous network called [LeNet](http://yann.lecun.com/exdb/lenet/) through `nn.Sequential`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "net = nn.Sequential()\n",
- "# Add a sequence of layers.\n",
- "net.add(# Similar to Dense, it is not necessary to specify the input channels\n",
- " # by the argument `in_channels`, which will be automatically inferred\n",
- " # in the first forward pass. Also, we apply a relu activation on the\n",
- " # output. In addition, we can use a tuple to specify a non-square\n",
- " # kernel size, such as `kernel_size=(2,4)`\n",
- " nn.Conv2D(channels=6, kernel_size=5, activation='relu'),\n",
- " # One can also use a tuple to specify non-symmetric pool and stride sizes\n",
- " nn.MaxPool2D(pool_size=2, strides=2),\n",
- " nn.Conv2D(channels=16, kernel_size=3, activation='relu'),\n",
- " nn.MaxPool2D(pool_size=2, strides=2),\n",
- " # The dense layer will automatically reshape the 4-D output of last\n",
- " # max pooling layer into the 2-D shape: (x.shape[0], x.size/x.shape[0])\n",
- " nn.Dense(120, activation=\"relu\"),\n",
- " nn.Dense(84, activation=\"relu\"),\n",
- " nn.Dense(10))\n",
- "net"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<!--Mention the tuple option for kernel and stride as an exercise for the reader? Or leave it out as too much info for now?-->\n",
- "\n",
- "Using `nn.Sequential` is similar to `nn.Dense`. In fact, both of them are subclasses of `nn.Block`. Use the following code to initialize the weights and run the forward pass."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "net.initialize()\n",
- "# Input shape is (batch_size, color_channels, height, width)\n",
- "x = np.random.uniform(size=(4,1,28,28))\n",
- "y = net(x)\n",
- "y.shape"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can use `[]` to index a particular layer. For example, the following\n",
- "accesses the first layer's weight and sixth layer's bias."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "(net[0].weight.data().shape, net[5].bias.data().shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Create a neural network flexibly\n",
- "\n",
- "In `nn.Sequential`, MXNet will automatically construct the forward function that sequentially executes added layers.\n",
- "Here is another way to construct a network with a flexible forward function.\n",
- "\n",
- "Create a subclass of `nn.Block` and implement two methods by using the following code.\n",
- "\n",
- "- `__init__` create the layers\n",
- "- `forward` define the forward function."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "6"
- }
- },
- "outputs": [],
- "source": [
- "class MixMLP(nn.Block):\n",
- " def __init__(self, **kwargs):\n",
- " # Run `nn.Block`'s init method\n",
- " super(MixMLP, self).__init__(**kwargs)\n",
- " self.blk = nn.Sequential()\n",
- " self.blk.add(nn.Dense(3, activation='relu'),\n",
- " nn.Dense(4, activation='relu'))\n",
- " self.dense = nn.Dense(5)\n",
- " def forward(self, x):\n",
- " y = npx.relu(self.blk(x))\n",
- " print(y)\n",
- " return self.dense(y)\n",
- "\n",
- "net = MixMLP()\n",
- "net"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In the sequential chaining approach, you can only add instances with `nn.Block` as the base class and then run them in a forward pass. In this example, you used `print` to get the intermediate results and `nd.relu` to apply relu activation. This approach provides a more flexible way to define the forward function.\n",
- "\n",
- "The following code example uses `net` in a similar manner as earlier."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "net.initialize()\n",
- "x = np.random.uniform(size=(2,2))\n",
- "net(x)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finally, access a particular layer's weight with this code."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "8"
- }
- },
- "outputs": [],
- "source": [
- "net.blk[1].weight.data()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Next steps\n",
- "\n",
- "After you create a neural network, learn how to automatically\n",
- "compute the gradients in [Step 3: Automatic differentiation with autograd](3-autograd.md)."
- ]
- }
- ],
- "metadata": {
- "language_info": {
- "name": "python"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/api/python/docs/_sources/tutorials/getting-started/crash-course/3-autograd.ipynb b/api/python/docs/_sources/tutorials/getting-started/crash-course/3-autograd.ipynb
index 79d6def..336a454 100644
--- a/api/python/docs/_sources/tutorials/getting-started/crash-course/3-autograd.ipynb
+++ b/api/python/docs/_sources/tutorials/getting-started/crash-course/3-autograd.ipynb
@@ -23,159 +23,327 @@
"\n",
"# Step 3: Automatic differentiation with autograd\n",
"\n",
- "In this step, you learn how to use the MXNet `autograd` package to perform gradient calculations by automatically calculating derivatives.\n",
- "\n",
- "This is helpful because it will help you save time and effort. You train models to get better as a function of experience. Usually, getting better means minimizing a loss function. To achieve this goal, you often iteratively compute the gradient of the loss with respect to weights and then update the weights accordingly. Gradient calculations are straightforward through a chain rule. However, for complex models, working this out manually is challenging.\n",
- "\n",
- "The `autograd` package helps you by automatically calculating derivatives.\n",
+ "In this step, you learn how to use the MXNet `autograd` package to perform\n",
+ "gradient calculations.\n",
"\n",
"## Basic use\n",
"\n",
- "To get started, import the `autograd` package as in the following code."
+ "To get started, import the `autograd` package with the following code."
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
+ "```python\n",
"from mxnet import np, npx\n",
"from mxnet import autograd\n",
- "npx.set_np()"
+ "npx.set_np()\n",
+ "```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "As an example, you could differentiate a function $f(x) = 2 x^2$ with respect to parameter $x$. You can start by assigning an initial value of $x$, as follows:"
+ "As an example, you could differentiate a function $f(x) = 2 x^2$ with respect to\n",
+ "parameter $x$. For Autograd, you can start by assigning an initial value of $x$,\n",
+ "as follows:"
]
},
{
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "3"
- }
- },
- "outputs": [],
+ "cell_type": "markdown",
+ "metadata": {},
"source": [
+ "```python\n",
"x = np.array([[1, 2], [3, 4]])\n",
- "x"
+ "x\n",
+ "```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "After you compute the gradient of $f(x)$ with respect to $x$, you need a place to store it. In MXNet, you can tell an ndarray that you plan to store a gradient by invoking its `attach_grad` method, shown in the following example."
+ "After you compute the gradient of $f(x)$ with respect to $x$, you need a place\n",
+ "to store it. In MXNet, you can tell a ndarray that you plan to store a gradient\n",
+ "by invoking its `attach_grad` method, as shown in the following example."
]
},
{
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "6"
- }
- },
- "outputs": [],
+ "cell_type": "markdown",
+ "metadata": {},
"source": [
- "x.attach_grad()"
+ "```python\n",
+ "x.attach_grad()\n",
+ "```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Next, define the function $y=f(x)$. To let MXNet store $y$, so that you can compute gradients later, use the following code to put the definition inside an `autograd.record()` scope."
+ "Next, define the function $y=f(x)$. To let MXNet store $y$, so that you can\n",
+ "compute gradients later, use the following code to put the definition inside an\n",
+ "`autograd.record()` scope."
]
},
{
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "7"
- }
- },
- "outputs": [],
+ "cell_type": "markdown",
+ "metadata": {},
"source": [
+ "```python\n",
"with autograd.record():\n",
- " y = 2 * x * x"
+ " y = 2 * x * x\n",
+ "```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "You can invoke back propagation (backprop) by calling `y.backward()`. When $y$ has more than one entry, `y.backward()` is equivalent to `y.sum().backward()`.\n",
- "<!-- I'm not sure what this second part really means. I don't have enough context. TMI?-->"
+ "You can invoke back propagation (backprop) by calling `y.backward()`. When $y$\n",
+ "has more than one entry, `y.backward()` is equivalent to `y.sum().backward()`."
]
},
{
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "8"
- }
- },
- "outputs": [],
+ "cell_type": "markdown",
+ "metadata": {},
"source": [
- "y.backward()"
+ "```python\n",
+ "y.backward()\n",
+ "```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Next, verify whether this is the expected output. Note that $y=2x^2$ and $\\frac{dy}{dx} = 4x$, which should be `[[4, 8],[12, 16]]`. Check the automatically computed results."
+ "Next, verify whether this is the expected output. Note that $y=2x^2$ and\n",
+ "$\\frac{dy}{dx} = 4x$, which should be `[[4, 8],[12, 16]]`. Check the\n",
+ "automatically computed results."
]
},
{
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "9"
- }
- },
- "outputs": [],
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "x.grad\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
"source": [
- "x.grad"
+ "Now you get to dive into `y.backward()` by first discussing a bit on gradients. As\n",
+ "alluded to earlier `y.backward()` is equivalent to `y.sum().backward()`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Using Python control flows\n",
+ "```python\n",
+ "with autograd.record():\n",
+ " y = np.sum(2 * x * x)\n",
+ "y.backward()\n",
+ "x.grad\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Additionally, you can only run backward once. Unless you use the flag\n",
+ "`retain_graph` to be `True`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "with autograd.record():\n",
+ " y = np.sum(2 * x * x)\n",
+ "y.backward(retain_graph=True)\n",
+ "print(x.grad)\n",
+ "print(\"Since you have retained your previous graph you can run backward again\")\n",
+ "y.backward()\n",
+ "print(x.grad)\n",
"\n",
- "Sometimes you want to write dynamic programs where the execution depends on real-time values. MXNet records the execution trace and computes the gradient as well.\n",
+ "try:\n",
+ " y.backward()\n",
+ "except:\n",
+ " print(\"However, you can't do backward twice unless you retain the graph.\")\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Custom MXNet ndarray operations\n",
"\n",
- "Consider the following function `f` in the following example code. The function doubles the inputs until its `norm` reaches 1000. Then it selects one element depending on the sum of its elements. \n",
- "<!-- I wonder if there could be another less \"mathy\" demo of this -->"
+ "In order to understand the `backward()` method it is beneficial to first\n",
+ "understand how you can create custom operations. MXNet operators are classes\n",
+ "with a forward and backward method. Where the number of args in `backward()`\n",
+ "must equal the number of items returned in the `forward()` method. Additionally,\n",
+ "the number of arguments in the `forward()` method must match the number of\n",
+ "output arguments from `backward()`. You can modify the gradients in backward to\n",
+ "return custom gradients. For instance, below you can return a different gradient then\n",
+ "the actual derivative."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "class My_First_Custom_Operation(autograd.Function):\n",
+ " def __init__(self):\n",
+ " super().__init__()\n",
+ " def forward(self,x,y):\n",
+ " return 2 * x, 2 * x * y, 2 * y\n",
+ " def backward(self, dx, dxy, dy):\n",
+ " \"\"\"\n",
+ " The input number of arguments must match the number of outputs from forward.\n",
+ " Furthermore, the number of output arguments must match the number of inputs from forward.\n",
+ " \"\"\"\n",
+ " return x, y\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now you can use the first custom operation you have built."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "x = np.random.uniform(-1, 1, (2, 3)) \n",
+ "y = np.random.uniform(-1, 1, (2, 3))\n",
+ "x.attach_grad()\n",
+ "y.attach_grad()\n",
+ "with autograd.record():\n",
+ " z = My_First_Custom_Operation()\n",
+ " z1, z2, z3 = z(x, y)\n",
+ " out = z1 + z2 + z3 \n",
+ "out.backward()\n",
+ "print(np.array_equiv(x.asnumpy(), x.asnumpy()))\n",
+ "print(np.array_equiv(y.asnumpy(), y.asnumpy()))\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Alternatively, you may want to have a function which is different depending on\n",
+ "if you are training or not."
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
+ "```python\n",
+ "def my_first_function(x):\n",
+ " if autograd.is_training(): # Return something else when training\n",
+ " return(4 * x)\n",
+ " else:\n",
+ " return(x)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "y = my_first_function(x)\n",
+ "print(np.array_equiv(y.asnumpy(), x.asnumpy()))\n",
+ "with autograd.record(train_mode=False):\n",
+ " y = my_first_function(x)\n",
+ "y.backward()\n",
+ "print(x.grad)\n",
+ "with autograd.record(train_mode=True): # train_mode = True by default\n",
+ " y = my_first_function(x)\n",
+ "y.backward()\n",
+ "print(x.grad)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You could create functions with `autograd.record()`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "def my_second_function(x):\n",
+ " with autograd.record():\n",
+ " return(2 * x)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "y = my_second_function(x)\n",
+ "y.backward()\n",
+ "print(x.grad)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also combine multiple functions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "y = my_second_function(x)\n",
+ "with autograd.record():\n",
+ " z = my_second_function(y) + 2\n",
+ "z.backward()\n",
+ "print(x.grad)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Additionally, MXNet records the execution trace and computes the gradient\n",
+ "accordingly. The following function `f` doubles the inputs until its `norm`\n",
+ "reaches 1000. Then it selects one element depending on the sum of its elements."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
"def f(a):\n",
" b = a * 2\n",
" while np.abs(b).sum() < 1000:\n",
@@ -184,7 +352,8 @@
" c = b[0]\n",
" else:\n",
" c = b[1]\n",
- " return c"
+ " return c\n",
+ "```\n"
]
},
{
@@ -195,41 +364,98 @@
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
+ "```python\n",
"a = np.random.uniform(size=2)\n",
"a.attach_grad()\n",
"with autograd.record():\n",
" c = f(a)\n",
- "c.backward()"
+ "c.backward()\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can see that `b` is a linear function of `a`, and `c` is chosen from `b`.\n",
+ "The gradient with respect to `a` be will be either `[c/a[0], 0]` or `[0,\n",
+ "c/a[1]]`, depending on which element from `b` is picked. You see the results of\n",
+ "this example with this code:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "a.grad == c / a\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can notice there are 3 values along the dimension 0, so taking a `mean`\n",
+ "along this axis is the same as summing that axis and multiplying by `1/3`.\n",
+ "\n",
+ "## Advanced MXNet ndarray operations with Autograd\n",
+ "\n",
+ "You can control gradients for different ndarray operations. For instance,\n",
+ "perhaps you want to check that the gradients are propagating properly?\n",
+ "the `attach_grad()` method automatically detaches itself from the gradient.\n",
+ "Therefore, the input up until y will no longer look like it has `x`. To\n",
+ "illustrate this notice that `x.grad` and `y.grad` is not the same in the second\n",
+ "example."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "You can see that `b` is a linear function of `a`, and `c` is chosen from `b`. The gradient with respect to `a` be will be either `[c/a[0], 0]` or `[0, c/a[1]]`, depending on which element from `b` is picked. You see the results of this example with this code:"
+ "```python\n",
+ "with autograd.record():\n",
+ " y = 3 * x\n",
+ " y.attach_grad()\n",
+ " z = 4 * y + 2 * x\n",
+ "z.backward()\n",
+ "print(x.grad)\n",
+ "print(y.grad)\n",
+ "```\n"
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "a.grad == c/a"
+ "Is not the same as:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "with autograd.record():\n",
+ " y = 3 * x\n",
+ " z = 4 * y + 2 * x\n",
+ "z.backward()\n",
+ "print(x.grad)\n",
+ "print(y.grad)\n",
+ "```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Next Steps\n",
+ "## Next steps\n",
"\n",
- "After you have used `autograd`, learn about training a neural network. See [Step 4: Train the neural network](4-train.md)."
+ "Learn how to initialize weights, choose loss function, metrics and optimizers for training your neural network [Step 4: Necessary components\n",
+ "to train the neural network](4-components.md)."
]
}
],
diff --git a/api/python/docs/_sources/tutorials/getting-started/crash-course/4-components.ipynb b/api/python/docs/_sources/tutorials/getting-started/crash-course/4-components.ipynb
new file mode 100644
index 0000000..bb73c5c
--- /dev/null
+++ b/api/python/docs/_sources/tutorials/getting-started/crash-course/4-components.ipynb
@@ -0,0 +1,611 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<!--- Licensed to the Apache Software Foundation (ASF) under one -->\n",
+ "<!--- or more contributor license agreements. See the NOTICE file -->\n",
+ "<!--- distributed with this work for additional information -->\n",
+ "<!--- regarding copyright ownership. The ASF licenses this file -->\n",
+ "<!--- to you under the Apache License, Version 2.0 (the -->\n",
+ "<!--- \"License\"); you may not use this file except in compliance -->\n",
+ "<!--- with the License. You may obtain a copy of the License at -->\n",
+ "\n",
+ "<!--- http://www.apache.org/licenses/LICENSE-2.0 -->\n",
+ "\n",
+ "<!--- Unless required by applicable law or agreed to in writing, -->\n",
+ "<!--- software distributed under the License is distributed on an -->\n",
+ "<!--- \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->\n",
+ "<!--- KIND, either express or implied. See the License for the -->\n",
+ "<!--- specific language governing permissions and limitations -->\n",
+ "<!--- under the License. -->\n",
+ "# Necessary components that are not in the network\n",
+ "\n",
+ "\n",
+ "Data and models are not the only components that\n",
+ "you need to train a deep learning model. In this notebook, you will\n",
+ "learn about the common components involved in training deep learning models. \n",
+ "Here is a list of components necessary for training models in MXNet.\n",
+ "\n",
+ "1. Initialization\n",
+ "2. Loss functions\n",
+ " 1. Built-in\n",
+ " 2. Custom\n",
+ "3. Optimizers\n",
+ "4. Metrics"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "from mxnet import np, npx,gluon\n",
+ "import mxnet as mx\n",
+ "from mxnet.gluon import nn\n",
+ "npx.set_np()\n",
+ "\n",
+ "ctx = mx.cpu()\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Initialization\n",
+ "\n",
+ "In a previous notebook, you used `net.initialize()` to initialize the network\n",
+ "before a forward pass. Now, you will learn about initialization in a little more\n",
+ "detail.\n",
+ "\n",
+ "First, define and initialize the `sequential` network from earlier.\n",
+ "After you initialize it, print the parameters using `collect_params()` method."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "net = nn.Sequential()\n",
+ "\n",
+ "net.add(nn.Dense(5, in_units=3, activation=\"relu\"),\n",
+ " nn.Dense(25, activation=\"relu\"),\n",
+ " nn.Dense(2)\n",
+ " )\n",
+ "\n",
+ "net\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "net.initialize()\n",
+ "params = net.collect_params()\n",
+ "\n",
+ "for key, value in params.items():\n",
+ " print(key, value)\n",
+ "\n",
+ "\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, you will print shape and params after the first forward pass."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "x = np.random.uniform(-1, 1, (10, 3))\n",
+ "net(x) # Forward computation\n",
+ "\n",
+ "params = net.collect_params()\n",
+ "for key, value in params.items():\n",
+ " print(key, value)\n",
+ "\n",
+ "\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Built-in Initialization\n",
+ "\n",
+ "MXNet makes it easy to initialize by providing many common initializers. A subset that you will be using in the following sections include:\n",
+ "\n",
+ "- Constant\n",
+ "- Normal\n",
+ "\n",
+ "For more information, see\n",
+ "[Initializers](https://mxnet.apache.org/versions/1.6/api/python/docs/api/initializer/index.html)\n",
+ "\n",
+ "When you use `net.intialize()`, MXNet, by default, initializes the weight matrices uniformly\n",
+ "by drawing random values with a uniform-distribution between −0.07 and 0.07 and\n",
+ "updates the bias parameters by setting them all to 0.\n",
+ "\n",
+ "To initialize your network using different built-in types, you have to use the\n",
+ "`init` keyword argument in the `initialize()` method. Here is an example using\n",
+ "`constant` and `normal` initialization."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "from mxnet import init\n",
+ "\n",
+ "# Constant init initializes the weights to be a constant value for all the params\n",
+ "net.initialize(init=init.Constant(3), ctx=ctx)\n",
+ "print(net[0].weight.data()[0])\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you use Normal to initialize your weights then you will use a normal\n",
+ "distribution with a mean of zero and standard deviation of sigma. If you have\n",
+ "already initialized the weight but want to reinitialize the weight, set the\n",
+ "`force_reinit` flag to `True`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "net.initialize(init=init.Normal(sigma=0.2), force_reinit=True, ctx=ctx)\n",
+ "print(net[0].weight.data()[0])\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Components used in a training loop\n",
+ "\n",
+ "Till now you have seen how to create an algorithm and how to initialize it using mxnet\n",
+ "APIs; additionally you have learned the basics of using mxnet. When you start training the\n",
+ "ML algorithm, how do you actually teach the algorithm to learn or train?\n",
+ "\n",
+ "There are three main components for training an algorithm.\n",
+ "\n",
+ "1. Loss function: calculates how far the model is from the true distribution\n",
+ "2. Autograd: the mxnet auto differentiation tool that calculates the gradients to\n",
+ "optimize the parameters\n",
+ "3. Optimizer: updates the parameters based on an optimization algorithm\n",
+ "\n",
+ "You have already learned about autograd in the previous notebook. In this\n",
+ "notebook, you will learn more about loss functions and optimizers.\n",
+ "\n",
+ "## Loss function\n",
+ "\n",
+ "Loss functions are used to train neural networks and help the algorithm learn\n",
+ "from the data. The loss function computes the difference between the\n",
+ "output from the neural network and ground truth. This output is used to\n",
+ "update the neural network weights during training. Next, you will look at a\n",
+ "simple example.\n",
+ "\n",
+ "Suppose you have a neural network `net` and the data is stored in a variable\n",
+ "`data`. The data consists of 5 total records (rows) and two features (columns)\n",
+ "and the output from the neural network after the first epoch is given by the\n",
+ "variable `nn_output`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "net = gluon.nn.Dense(1)\n",
+ "net.initialize()\n",
+ "\n",
+ "nn_input = np.array([[1.2, 0.56],\n",
+ " [3.0, 0.72],\n",
+ " [0.89, 0.9],\n",
+ " [0.89, 2.3],\n",
+ " [0.99, 0.52]])\n",
+ "\n",
+ "nn_output = net(nn_input)\n",
+ "nn_output\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The ground truth value of the data is stored in `groundtruth_label` is"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "groundtruth_label = np.array([[0.0083],\n",
+ " [0.00382],\n",
+ " [0.02061],\n",
+ " [0.00495],\n",
+ " [0.00639]]).reshape(5, 1)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For this problem, you will use the L2 Loss. L2Loss, also called Mean Squared Error, is a\n",
+ "regression loss function that computes the squared distances between the target\n",
+ "values and the output of the neural network. It is defined as:\n",
+ "\n",
+ "$$L = \\frac{1}{2N}\\sum_i{|label_i − pred_i|)^2}$$\n",
+ "\n",
+ "The L2 loss function creates larger gradients for loss values which are farther apart due to the\n",
+ "square operator and it also smooths the loss function space."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "def L2Loss(output_values, true_values):\n",
+ " return np.mean((output_values - true_values) ** 2, axis=1) / 2\n",
+ "\n",
+ "L2Loss(nn_output, groundtruth_label)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, you can do the same thing using the mxnet API"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "from mxnet.gluon import nn, loss as gloss\n",
+ "loss = gloss.L2Loss()\n",
+ "\n",
+ "loss(nn_output, groundtruth_label)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A network can improve by iteratively updating its weights to minimise the loss.\n",
+ "Some tasks use a combination of multiple loss functions, but often you will just\n",
+ "use one. MXNet Gluon provides a number of the most commonly used loss functions.\n",
+ "The choice of your loss function will depend on your network and task. Some\n",
+ "common tasks and loss function pairs include:\n",
+ "\n",
+ "- regression: L1Loss, L2Loss\n",
+ "\n",
+ "- classification: SigmoidBinaryCrossEntropyLoss, SoftmaxCrossEntropyLoss\n",
+ "\n",
+ "- embeddings: HingeLoss\n",
+ "\n",
+ "#### Customizing your Loss functions\n",
+ "\n",
+ "You can also create custom loss functions using **Loss Blocks**.\n",
+ "\n",
+ "You can inherit the base `Loss` class and write your own `forward` method. The\n",
+ "backward propagation will be automatically computed by autograd. However, that\n",
+ "only holds true if you can build your loss from existing mxnet operators."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "from mxnet.gluon.loss import Loss\n",
+ "\n",
+ "class custom_L1_loss(Loss):\n",
+ " def __init__(self, weight=None, batch_axis=0, **kwargs):\n",
+ " super(custom_L1_loss, self).__init__(weight, batch_axis, **kwargs)\n",
+ "\n",
+ " def forward(self, pred, label):\n",
+ " l = np.abs(label - pred)\n",
+ " l = l.reshape(len(l),)\n",
+ " return l\n",
+ " \n",
+ "L1 = custom_L1_loss()\n",
+ "L1(nn_output, groundtruth_label)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "l1=gloss.L1Loss()\n",
+ "l1(nn_output, groundtruth_label)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Optimizer\n",
+ "\n",
+ "The loss function determines how much to change the parameters based on how far the\n",
+ "model is from the groundtruth. Optimizer determines how the model\n",
+ "weights or parameters are updated based on the loss function. In Gluon, this\n",
+ "optimization step is performed by the `gluon.Trainer`.\n",
+ "\n",
+ "Here is a basic example of how to call the `gluon.Trainer` method."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "from mxnet import optimizer\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "trainer = gluon.Trainer(net.collect_params(),\n",
+ " optimizer=\"Adam\",\n",
+ " optimizer_params={\n",
+ " \"learning_rate\":0.1,\n",
+ " \"wd\":0.001\n",
+ " })\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When creating a **Gluon Trainer**, you must provide the trainer object with\n",
+ "1. A collection of parameters that need to be learnt. The collection of\n",
+ "parameters will be the weights and biases of your network that you are training.\n",
+ "2. An Optimization algorithm (optimizer) that you want to use for training. This\n",
+ "algorithm will be used to update the parameters every training iteration when\n",
+ "`trainer.step` is called. For more information, see\n",
+ "[optimizers](https://mxnet.apache.org/versions/1.6/api/python/docs/api/optimizer/index.html)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "curr_weight = net.weight.data()\n",
+ "print(curr_weight)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "batch_size = len(nn_input)\n",
+ "trainer.step(batch_size)\n",
+ "print(net.weight.data())\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "print(curr_weight - net.weight.grad() * 1 / 5)\n",
+ "\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Metrics\n",
+ "\n",
+ "MXNet includes a `metrics` API that you can use to evaluate how your model is\n",
+ "performing. This is typically used during training to monitor performance on the\n",
+ "validation set. MXNet includes many commonly used metrics, a few are listed below:\n",
+ "\n",
+ "-\n",
+ "[Accuracy](https://mxnet.apache.org/versions/1.6/api/python/docs/api/metric/index.html#mxnet.metric.Accuracy)\n",
+ "-\n",
+ "[CrossEntropy](https://mxnet.apache.org/versions/1.6/api/python/docs/api/metric/index.html#mxnet.metric.CrossEntropy)\n",
+ "- [Mean squared\n",
+ "error](https://mxnet.apache.org/versions/1.6/api/python/docs/api/metric/index.html#mxnet.metric.MSE)\n",
+ "- [Root mean squared error\n",
+ "(RMSE)](https://mxnet.apache.org/versions/1.6/api/python/docs/api/metric/index.html#mxnet.metric.RMSE)\n",
+ "\n",
+ "Now, you will define two arrays for a dummy binary classification example."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "# Vector of likelihoods for all the classes\n",
+ "pred = np.array([[0.1, 0.9], [0.05, 0.95], [0.83, 0.17], [0.63, 0.37]])\n",
+ "\n",
+ "labels = np.array([1, 1, 0, 1])\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Before you can calculate the accuracy of your model, the metric (accuracy)\n",
+ "should be instantiated before the training loop"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "from mxnet.gluon.metric import Accuracy\n",
+ "\n",
+ "acc = Accuracy()\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To run and calculate the updated accuracy for each batch or epoch, you can call\n",
+ "the `update()` method. This method uses labels and predictions which can be\n",
+ "either class indexes or a vector of likelihoods for all of the classes."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "acc.update(labels=labels, preds=pred)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Creating custom metrics\n",
+ "\n",
+ "In addition to built-in metrics, if you want to create a custom metric, you can\n",
+ "use the following skeleton code. This code inherits from the `EvalMetric` base\n",
+ "class."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```\n",
+ "def custom_metric(EvalMetric):\n",
+ " def __init__(self):\n",
+ " super().init()\n",
+ "\n",
+ " def update(self, labels, preds):\n",
+ " pass\n",
+ "\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is an example using the Precision metric. First, define the two values\n",
+ "`labels` and `preds`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "labels = np.array([0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1])\n",
+ "preds = np.array([0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0])\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, define the custom metric class `precision` and instantiate it"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "from mxnet.gluon.metric import EvalMetric\n",
+ "\n",
+ "class precision(EvalMetric):\n",
+ " def __init__(self):\n",
+ " super().__init__(name=\"Precision\")\n",
+ " \n",
+ " def update(self,labels, preds):\n",
+ " tp_labels = (labels == 1)\n",
+ " true_positives = sum(preds[tp_labels] == 1)\n",
+ " fp_labels = (labels == 0)\n",
+ " false_positives = sum(preds[fp_labels] == 1)\n",
+ " return true_positives / (true_positives + false_positives)\n",
+ " \n",
+ "p = precision()\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And finally, call the `update` method to return the results of `precision` for your data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "p.update(np.array(y_true), np.array(y_pred))\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Next steps\n",
+ "\n",
+ "Now that you have learned all the components required to train a neural network,\n",
+ "you will see how to load your data using the Gluon API in [Step 5: Gluon\n",
+ "Datasets and DataLoader](5-datasets.md)"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/api/python/docs/_sources/tutorials/getting-started/crash-course/4-train.ipynb b/api/python/docs/_sources/tutorials/getting-started/crash-course/4-train.ipynb
deleted file mode 100644
index bc4346d..0000000
--- a/api/python/docs/_sources/tutorials/getting-started/crash-course/4-train.ipynb
+++ /dev/null
@@ -1,403 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<!--- Licensed to the Apache Software Foundation (ASF) under one -->\n",
- "<!--- or more contributor license agreements. See the NOTICE file -->\n",
- "<!--- distributed with this work for additional information -->\n",
- "<!--- regarding copyright ownership. The ASF licenses this file -->\n",
- "<!--- to you under the Apache License, Version 2.0 (the -->\n",
- "<!--- \"License\"); you may not use this file except in compliance -->\n",
- "<!--- with the License. You may obtain a copy of the License at -->\n",
- "\n",
- "<!--- http://www.apache.org/licenses/LICENSE-2.0 -->\n",
- "\n",
- "<!--- Unless required by applicable law or agreed to in writing, -->\n",
- "<!--- software distributed under the License is distributed on an -->\n",
- "<!--- \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->\n",
- "<!--- KIND, either express or implied. See the License for the -->\n",
- "<!--- specific language governing permissions and limitations -->\n",
- "<!--- under the License. -->\n",
- "\n",
- "# Step 4: Train the neural network\n",
- "\n",
- "In this step, you learn how to train the previously defined network with data. First, import the libraries. The new ones are `mxnet.init` for more weight initialization methods. Import the `datasets` and `transforms` to load and transform computer vision datasets. Import `matplotlib` for drawing, and `time` for benchmarking. The example command here shows this."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "1"
- }
- },
- "outputs": [],
- "source": [
- "# Uncomment the following line if matplotlib is not installed.\n",
- "# !pip install matplotlib\n",
- "\n",
- "from mxnet import np, npx, gluon, init, autograd\n",
- "from mxnet.gluon import nn\n",
- "from IPython import display\n",
- "import matplotlib.pyplot as plt\n",
- "import time\n",
- "npx.set_np()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Get data\n",
- "\n",
- "The handwritten digit, MNIST dataset is one of the most commonly used datasets in deep learning. However, it's too simple to get 99 percent accuracy. For this tutorial, you use a similar but slightly more complicated dataset called FashionMNIST. The end-goal is to classify clothing types.\n",
- "\n",
- "The dataset can be automatically downloaded through Gluon's `data.vision.datasets` module. The following code downloads the training dataset and shows the first example."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "2"
- }
- },
- "outputs": [],
- "source": [
- "mnist_train = gluon.data.vision.datasets.FashionMNIST(train=True)\n",
- "X, y = mnist_train[0]\n",
- "('X shape: ', X.shape, 'X dtype', X.dtype, 'y:', y)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Each example in this dataset is a $28\\times 28$ size grey image, which is presented as ndarray with the shape format of `(height, width, channel)`. The label is a `numpy` scalar.\n",
- "\n",
- "Next, visualize the first six examples."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "3"
- }
- },
- "outputs": [],
- "source": [
- "text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n",
- " 'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
- "X, y = mnist_train[0:10]\n",
- "# plot images\n",
- "display.set_matplotlib_formats('svg')\n",
- "_, figs = plt.subplots(1, X.shape[0], figsize=(15, 15))\n",
- "for f, x, yi in zip(figs, X, y):\n",
- " # 3D->2D by removing the last channel dim\n",
- " f.imshow(x.reshape((28,28)).asnumpy())\n",
- " ax = f.axes\n",
- " ax.set_title(text_labels[int(yi)])\n",
- " ax.title.set_fontsize(14)\n",
- " ax.get_xaxis().set_visible(False)\n",
- " ax.get_yaxis().set_visible(False)\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In order to feed data into a Gluon model, convert the images to the `(channel, height, width)` format with a floating point data type. It can be done by `transforms.ToTensor`. In addition, normalize all pixel values with `transforms.Normalize` with the real mean 0.13 and standard deviation 0.31. You can chain these two transforms together and apply it to the first element of the data pair, namely the images."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "attributes": {
- "classes": [],
- "id": "",
- "n": "4"
- }
- },
- "outputs": [],
... 107440 lines suppressed ...