MXNet digest for June 2018

[Simon Corston-Oliver <si...@gmail.com>]

To keep update with what’s new in the world of MXNet, please follow the
follow accounts:



Twitter: @ApacheMXNet <https://twitter.com/ApacheMXNet>

Medium: apache-mxnet <https://medium.com/apache-mxnet>

Youtube: apachemxnet <https://www.youtube.com/apachemxnet>

Reddit: r/mxnet <https://www.reddit.com/r/mxnet/>



Here’s a partial digest of tutorials, blogs, videos and announcements about
MXNet during June 2018.




Announcing Keras-MXNet v2.2
<https://medium.com/apache-mxnet/announcing-keras-mxnet-v2-2-4b8404568e75>

Use MXNet as the backend for Keras. What happens next will shock you.
(Spoiler alert: faster training time, better memory management and all the
other goodness you expect with MXNet as your engine.)



The importance of hyperparameter tuning for scaling deep learning training
to multiple GPUs
<https://aws.amazon.com/blogs/machine-learning/the-importance-of-hyperparameter-tuning-for-scaling-deep-learning-training-to-multiple-gpus/>

If you move from training on a single GPU to training on multiple GPUs you
need to tune hyperparameters such as batch size and learning rate.



Saving and Loading Gluon Models
<https://mxnet.incubator.apache.org/tutorials/gluon/save_load_params.html>.

Step by step guide to loading and saving models and parameters.



Scala API for Deep Learning Inference Now Available with MXNet v1.2
<https://medium.com/apache-mxnet/scala-api-for-deep-learning-inference-now-available-with-mxnet-v1-2-bcb13235db95>

Fixes to Scala memory issues, improvements to the API and an upgrade to the
MXNet 1.2 engine.



*Computer Vision using Scala/MXNet*

A series of tutorials on CV using Scala inference API

   - Image Classification with MXNet Scala Inference API
   <https://medium.com/apache-mxnet/image-classification-with-mxnet-scala-inference-api-8ab6ce1bbccf>
   - Object Detection with MXNet Scala Inference API
   <https://medium.com/apache-mxnet/object-detection-with-mxnet-scala-inference-api-9049230c77fd>
   - Image Classification with MXNet Scala Inference API
   <https://medium.com/apache-mxnet/image-classification-with-mxnet-scala-inference-api-8ab6ce1bbccf>



Train using Keras-MXNet and inference using MXNet Scala API
<https://medium.com/apache-mxnet/train-using-keras-mxnet-and-inference-using-mxnet-scala-api-49476a16a46a>

Get the best of two worlds: train in a Pythonic world then deploy for
inference in a JVM world using Scala.



Page Segmentation with Gluon
<https://medium.com/apache-mxnet/page-segmentation-with-gluon-dcb4e5955e2>

If you want to do OCR on handwritten text, first you have to segment the
page to find the sections.



Profiling MXNet Models
<http://mxnet.incubator.apache.org/tutorials/python/profiler.html>

How-to guide to really understanding what’s going on in your models.



Inference using ONNX Model Zoo
<https://medium.com/@khedia.ankit/64eeeddb9c7a>

Grab a pretrained model from the ONNX model zoo (which may have been
trained and saved in any framework that supports the ONNX interchange
format) load it in MXNet and do inference.



Getting Started with SageMaker
<https://medium.com/apache-mxnet/getting-started-with-sagemaker-ebe1277484c9>

A step-by-step guide to training MXNet models using Sagemaker.



*Learning Rates*

A three-part series about the state of the art in learning rates, learning
rate schedules and finding optimal learning rates for faster convergence:

   - Learning rate schedules (Youtube
   <https://www.youtube.com/watch?v=VR7OO2qyub4>, Tutorial
   <https://mxnet.incubator.apache.org/tutorials/gluon/learning_rate_schedules.html>
   )
   - State-of-the-art Learning Rate Schedules *(Youtube
   <https://www.youtube.com/watch?v=kbe_tNGoBHI>*, *Tutorial
   <https://mxnet.incubator.apache.org/tutorials/gluon/learning_rate_schedules_advanced.html>)*
   - Choosing the Learning Rate with LR Finder (Youtube
   <https://www.youtube.com/watch?v=U1aPRX_SIZM>)