<commit_before>  DeepCL
==========

Global Contents
===============

- [This page](doc/README.md)
- [Command line usage](doc/Commandline.md)
- [Neural Net API](doc/NeuralNetAPI.md)
- [Python wrappers](python_swig/README.md)
- [Cython wrappers](python/README.md)
- [Lua wrappers](lua/README.md)
- [Q-learning](doc/QLearning.md)
- [To build](doc/Build.md)
- [Stable API](doc/PublicApis.md)
- [Benchmarking](doc/Benchmarking.md)
- [Testing](doc/Testing.md)
- [Architecture](doc/Architecture.md)
- [Changes](doc/Changes.md)

DeepCL
==========

OpenCL library to train deep convolutional networks
- C++
- OpenCL
- Deep convolutional
- includes Q-learning module
- Python wrappers available
- (New!) Lua wrappers available

Functionalities:
* convolutional layers
* max-pooling
* normalization layer
* random translations, as in [Flexible, High Performance Convolutional Neural Networks for Image Classification](http://ijcai.org/papers11/Papers/IJCAI11-210.pdf)
* random patches, as in [ImageNet Classification with Deep Convolutional Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)
* multinet, ie Multi-column deep convolutional network, [McDnn](http://arxiv.org/pdf/1202.2745.pdf)
* simple command-line network specification, as per notation in [Multi-column Deep Neural Networks for Image Classification](http://arxiv.org/pdf/1202.2745.pdf)
* pad-zeros possible for convolutional layer
* dropout (New!)
* various activation functions available:
  * tanh
  * scaled tanh (1.7519 * tanh(2/3x) )
  * linear<commit_msg>add link to 3.x.x doc<commit_after>  DeepCL
==========

Global Contents
===============

- [This page](doc/README.md)
- [Command line usage](doc/Commandline.md)
- [Neural Net API](doc/NeuralNetAPI.md)
- [Python wrappers](python_swig/README.md)
- [Cython wrappers](python/README.md)
- [Lua wrappers](lua/README.md)
- [Q-learning](doc/QLearning.md)
- [To build](doc/Build.md)
- [Stable API](doc/PublicApis.md)
- [Benchmarking](doc/Benchmarking.md)
- [Testing](doc/Testing.md)
- [Architecture](doc/Architecture.md)
- [Changes](doc/Changes.md)
- [Doc for current stable 3.x.x](https://github.com/hughperkins/DeepCL/blob/master/README.md)

DeepCL
==========

OpenCL library to train deep convolutional networks
- C++
- OpenCL
- Deep convolutional
- includes Q-learning module
- Python wrappers available
- (New!) Lua wrappers available

Functionalities:
* convolutional layers
* max-pooling
* normalization layer
* random translations, as in [Flexible, High Performance Convolutional Neural Networks for Image Classification](http://ijcai.org/papers11/Papers/IJCAI11-210.pdf)
* random patches, as in [ImageNet Classification with Deep Convolutional Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)
* multinet, ie Multi-column deep convolutional network, [McDnn](http://arxiv.org/pdf/1202.2745.pdf)
* simple command-line network specification, as per notation in [Multi-column Deep Neural Networks for Image Classification](http://arxiv.org/pdf/1202.2745.pdf)
* pad-zeros possible for convolutional layer
* dropout (New!)
* various activation functions available:
  * tanh
  * scaled tanh (1.7519 * tanh(2/3x) )
  * linear
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