[incubator-mxnet-examples] branch master updated: add readme and toolkits (#1)

[zh...@apache.org]

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The following commit(s) were added to refs/heads/master by this push:
     new 994235c  add readme and toolkits (#1)
994235c is described below

commit 994235c901a1f53ffb080b0f9088e59e13e23f5f
Author: Sheng Zha <sz...@users.noreply.github.com>
AuthorDate: Sun Aug 2 14:41:49 2020 -0700

    add readme and toolkits (#1)
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+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Apache MXNet (Incubating) Examples
+
+This page contains a curated list of awesome Apache MXNet examples, tutorials and blogs. It is inspired by [awesome-php](https://github.com/ziadoz/awesome-php) and [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning). See also [Awesome-MXNet](https://github.com/chinakook/Awesome-MXNet) for a similar list.
+
+If you are new to MXNet, please check out the Gluon [60-minute crash course](http://gluon-crash-course.mxnet.io/). Also, [D2L.ai](http://d2l.ai/) offers great materials on deep learning with interactive jupyter notebooks in MXNet, math formula, and a dedicated forum for discussions.
+
+  - [Contributing](#contributing)
+  - [Tools with Apache MXNet](#tools-with-mxnet)
+
+## <a name="contributing"></a>Contributing
+
+If you want to contribute to this list and the examples, please open a new pull request.
+
+## <a name="tools-with-mxnet"></a>Tools with MXNet
+* [GluonCV](http://gluon-cv.mxnet.io/) - GluonCV provides implementations of state-of-the-art (SOTA) deep learning algorithms in computer vision. It aims to help engineers, researchers, and students quickly prototype products, validate new ideas and learn computer vision. It features training scripts that reproduce SOTA results reported in latest papers, a large set of pre-trained models, carefully designed APIs and easy to understand implementations and community support.
+* [GluonNLP](http://gluon-nlp.mxnet.io/) - GluonNLP provides implementations of the state-of-the-art (SOTA) deep learning models in NLP, and build blocks for text data pipelines and models. It is designed for engineers, researchers, and students to fast prototype research ideas and products based on these models.
+* [GluonTS](http://gluon-ts.mxnet.io/) - the Gluon toolkit for probabilistic time series modeling, focusing on deep learning-based models.
+* [AutoGluon](http://autogluon.mxnet.io/) - AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on deep learning and real-world applications spanning image, text, or tabular data. Intended for both ML beginners and experts
+* [InsightFace](http://insightface.ai/) - InsightFace provides implementations of state-of-the-art (SOTA) face analysis algorithms in computer vision.
+* [Sockeye](https://awslabs.github.io/sockeye/) - a sequence-to-sequence framework for Neural Machine Translation based on Apache MXNet. It implements state-of-the-art encoder-decoder architectures.
+* [Optuna](https://optuna.org/) - An open source hyperparameter optimization framework to automate hyperparameter search.
+* [Tensorly](http://tensorly.org/stable/home.html) - Simple and fast Tensor Learning in Python.
+* [GluonFace](https://gluon-face.readthedocs.io/en/latest/) - Gluon Face is a toolkit based on MXNet Gluon, provides SOTA deep learning algorithm and models in face recognition.
+* [Apache TVM (incubating)](https://tvm.apache.org/about) - an open deep learning compiler stack for CPUs, GPUs, and specialized accelerators. It aims to close the gap between the productivity-focused deep learning frameworks, and the performance- or efficiency-oriented hardware backends.