<commit_before>Apache MXNet (incubating) for Deep Learning
=====

[![Build Status](https://travis-ci.org/dmlc/mxnet.svg?branch=master)](https://travis-ci.org/dmlc/mxnet)
[![Documentation Status](https://readthedocs.org/projects/mxnet/badge/?version=latest)](http://mxnet.io/)
[![GitHub license](http://dmlc.github.io/img/apache2.svg)](./LICENSE)

![banner](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/banner.png)

Apache MXNet (incubating) is a deep learning framework designed for both *efficiency* and *flexibility*.
It allows you to ***mix*** [symbolic and imperative programming](http://mxnet.io/architecture/index.html#deep-learning-system-design-concepts)
to ***maximize*** efficiency and productivity.
At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
A graph optimization layer on top of that makes symbolic execution fast and memory efficient.
MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.

MXNet is also more than a deep learning project. It is also a collection of
[blue prints and guidelines](http://mxnet.io/architecture/index.html#deep-learning-system-design-concepts) for building
deep learning systems, and interesting insights of DL systems for hackers.

[![Join the chat at https://gitter.im/dmlc/mxnet](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dmlc/mxnet?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

What's New
----------
* [Version 0.11.0-rc0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.11.0-rc0) - MXNet 0.11.0-rc0 Release.
* [Apache Incubator](http://incubator.apache.org/projects/mxnet.html) - We are now an Apache Incubator project.
* [Version 0.10.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.10.0) - MXNet 0.10.0 Release.
* [Version 0.9.3 Release](./docs/architecture/release_note_0_9.md) - First 0.9 official release.
* [Version 0.9.1 Release (NNVM refactor)](./docs/architecture/release_note_0_9.md) - NNVM branch is merged into master now. An official release will be made soon.
* [Version 0.8.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.8.0)
* [Updated Image Classification with new Pre-trained Models](./example/image-classification)
* [Python Notebooks for How to Use MXNet](https://github.com/dmlc/mxnet-notebooks)
* [MKLDNN for Faster CPU Performance](./MKL_README.md)
* [MXNet Memory Monger, Training Deeper Nets with Sublinear Memory Cost](https://github.com/dmlc/mxnet-memonger)
* [Tutorial for NVidia GTC 2016](https://github.com/dmlc/mxnet-gtc-tutorial)
* [Embedding Torch layers and functions in MXNet](http://mxnet.io/how_to/torch.html)
* [MXNet.js: Javascript Package for Deep Learning in Browser (without server)
](https://github.com/dmlc/mxnet.js/)
* [Design Note: Design Efficient Deep Learning Data Loading Module](http://mxnet.io/architecture/note_data_loading.html)
* [MXNet on Mobile Device](http://mxnet.io/how_to/smart_device.html)
* [Distributed Training](http://mxnet.io/how_to/multi_devices.html)
* [Guide to Creating New Operators (Layers)](http://mxnet.io/how_to/new_op.html)<commit_msg>broken link in readme (#7441)<commit_after>Apache MXNet (incubating) for Deep Learning
=====

[![Build Status](https://travis-ci.org/dmlc/mxnet.svg?branch=master)](https://travis-ci.org/dmlc/mxnet)
[![Documentation Status](https://readthedocs.org/projects/mxnet/badge/?version=latest)](http://mxnet.io/)
[![GitHub license](http://dmlc.github.io/img/apache2.svg)](./LICENSE)

![banner](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/banner.png)

Apache MXNet (incubating) is a deep learning framework designed for both *efficiency* and *flexibility*.
It allows you to ***mix*** [symbolic and imperative programming](http://mxnet.io/architecture/index.html#deep-learning-system-design-concepts)
to ***maximize*** efficiency and productivity.
At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
A graph optimization layer on top of that makes symbolic execution fast and memory efficient.
MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.

MXNet is also more than a deep learning project. It is also a collection of
[blue prints and guidelines](http://mxnet.io/architecture/index.html#deep-learning-system-design-concepts) for building
deep learning systems, and interesting insights of DL systems for hackers.

[![Join the chat at https://gitter.im/dmlc/mxnet](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dmlc/mxnet?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

What's New
----------
* [Version 0.11.0-rc0 Release](https://github.com/apache/incubator-mxnet/releases/tag/0.11.0.rc0) - MXNet 0.11.0-rc0 Release.
* [Apache Incubator](http://incubator.apache.org/projects/mxnet.html) - We are now an Apache Incubator project.
* [Version 0.10.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.10.0) - MXNet 0.10.0 Release.
* [Version 0.9.3 Release](./docs/architecture/release_note_0_9.md) - First 0.9 official release.
* [Version 0.9.1 Release (NNVM refactor)](./docs/architecture/release_note_0_9.md) - NNVM branch is merged into master now. An official release will be made soon.
* [Version 0.8.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.8.0)
* [Updated Image Classification with new Pre-trained Models](./example/image-classification)
* [Python Notebooks for How to Use MXNet](https://github.com/dmlc/mxnet-notebooks)
* [MKLDNN for Faster CPU Performance](./MKL_README.md)
* [MXNet Memory Monger, Training Deeper Nets with Sublinear Memory Cost](https://github.com/dmlc/mxnet-memonger)
* [Tutorial for NVidia GTC 2016](https://github.com/dmlc/mxnet-gtc-tutorial)
* [Embedding Torch layers and functions in MXNet](http://mxnet.io/how_to/torch.html)
* [MXNet.js: Javascript Package for Deep Learning in Browser (without server)
](https://github.com/dmlc/mxnet.js/)
* [Design Note: Design Efficient Deep Learning Data Loading Module](http://mxnet.io/architecture/note_data_loading.html)
* [MXNet on Mobile Device](http://mxnet.io/how_to/smart_device.html)
* [Distributed Training](http://mxnet.io/how_to/multi_devices.html)
* [Guide to Creating New Operators (Layers)](http://mxnet.io/how_to/new_op.html)
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