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new 994235c add readme and toolkits (#1)
994235c is described below
commit 994235c901a1f53ffb080b0f9088e59e13e23f5f
Author: Sheng Zha <[email protected]>
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.
