Talk:Comparison of deep learning software/Resources: Difference between revisions

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{{Underlinked|date=March 2016}}

This page lists resources that can be useful to the [[Comparison of deep learning software]] page.

==Deep learning software not yet covered==

{{Expand list|date=April 2017}}

This is a list of deep learning software that is not listed on the [[Comparison of deep learning software|main page]] because they lack a Wikipedia article. If you would like to see any of these pieces of software listed there, you are welcome to create a Wikipedia article for it.

* [[adnn]][https://github.com/dritchie/adnn] – Javascript neural networks

* [[Blocks]][https://github.com/mila-udem/blocks] – Theano framework for building and training neural networks

* [[Caffe2]][https://caffe2.ai/] – Deep learning framework built on [[Caffe (software)|Caffe]], developed by [[Facebook]] in cooperation with [[NVIDIA]], [[Qualcomm]], [[Intel]], [[Amazon.com|Amazon]], and [[Microsoft]]<ref>https://caffe2.ai/blog/2017/04/18/caffe2-open-source-announcement.html</ref>

* [[CaffeOnSpark]][https://github.com/yahoo/CaffeOnSpark] – Scalable deep learning package running Caffe on [[Apache Spark|Spark]] and [[Apache Hadoop|Hadoop]] clusters with [[peer-to-peer]] communication

* [[Chainer]][https://chainer.org/] – Flexible neural network framework, adopting a "Define-by-run" scheme where the actual forward computation defines the network

* [[CNNLab]][httphttps://arxiv.org/abs/1606.06234] – Deep learning framework using GPU and FPGA-based accelerators

* [[ConvNetJS]][http://cs.stanford.edu/people/karpathy/convnetjs/] – Javascript library for training deep learning models entirely in a web browser

* [[List of neuroimaging software|Cortex]][https://github.com/rdevon/cortex] – Theano-based deep learning toolbox for neuroimaging

* [[cuDNN]][https://developer.nvidia.com/cudnn] – Optimized deep learning computation primitives implemented in CUDA

* [[CURRENNT]][https://sourceforge.net/projects/currennt/] – CUDA-accelerated toolkit for deep Long Short-Term Memory (LSTM) RNN architectures supporting large data sets not fitting into main memory.

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* [[DeepCL]][https://github.com/hughperkins/DeepCL] – OpenCL library to train deep convolutional networks, with APIs for C++, Python and the command line

* [[deeplearn.js]][https://pair-code.github.io/deeplearnjs/] – Hardware-accelerated deep learning library for the web browser

* [[DeepLearningKit]][http://deeplearningkit.org/] – Open source deep learning framework for iOS, OS X and tvOS<ref>httphttps://arxiv.org/pdf/1605.04614v1.pdf</ref>

* [[DeepLearnToolbox]][https://github.com/rasmusbergpalm/DeepLearnToolbox] – Matlab/Octave toolbox for deep learning (deprecated)

* [[DeepX]][http://niclane.org/pubs/deepx_ipsn.pdf] – Software accelerator for deep learning execution aimed towards mobile devices

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* [[IDLF]][https://github.com/01org/idlf] – [[Intel]]® Deep Learning Framework; supports OpenCL (deprecated)

* Intel [[Math Kernel Library]] (Intel MKL),<ref>https://software.intel.com/en-us/articles/introducing-dnn-primitives-in-intelr-mkl</ref> library of optimized math routines, including optimized deep learning computation primitives

* [[Lasagne]][http://lasagne.readthedocs.org/en/latest/] – Lightweight library to build and train neural networks in Theano

* [[Leaf]][https://github.com/autumnai/leaf] – "The Hacker's Machine Learning Engine"; supports OpenCL (official development suspended<ref>{{cite web|author=Michael Hirn|url=https://medium.com/@mjhirn/tensorflow-wins-89b78b29aafb#.u5yveb1le|title=Tensorflow wins|date=9 May 2016|accessdate=17 August 2016|quote=... I will suspend the development of Leaf and focus on new ventures.}}</ref>)

* [[LightNet]][httphttps://arxiv.org/abs/1605.02766] – MATLAB-based environment for deep learning

* [[MaTEx]][https://github.com/abhinavvishnu/matex] – Distributed TensorFlow with MPI by [[PNNL]]

* [[Mocha]][https://github.com/pluskid/Mocha.jl] – Deep learning framework for [[Julia (programming language)|Julia]], inspired by Caffe

* [[neon]][https://github.com/NervanaSystems/neon] – Nervana's Python based Deep Learning framework

* [[Purine]][https://github.com/purine/purine2] – Bi-graph based deep learning framework<ref>https://arxiv.org/abs/1412.6249</ref>

* [[Pylearn2]][http://deeplearning.net/software/pylearn2/] – Machine learning library mainly built on top of Theano

* [[scikit-neuralnetwork]][https://scikit-neuralnetwork.readthedocs.org/] – Multi-layer perceptrons as a wrapper for Pylearn2

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* [[tiny-dnn]][https://github.com/nyanp/tiny-dnn] – Header only, dependency-free deep learning framework in C++11

* [[torchnet]][https://github.com/torchnet/torchnet] – Torch framework providing a set of abstractions aiming at encouraging code re-use as well as encouraging modular programming<ref>https://code.facebook.com/posts/580706092103929</ref><ref>{{cite web|author1=Ronan Collobert|author2=Laurens van der Maaten|author3=Armand Joulin|title=Torchnet: An Open-Source Platform for (Deep) Learning Research|url=https://lvdmaaten.github.io/publications/papers/Torchnet_2016.pdf|publisher=Facebook AI Research|accessdate=24 June 2016}}</ref>

* [[Veles]][https://github.com/Samsung/veles] – Distributed machine learning platform by [[Samsung]]

==Related software==

* [[Deep Visualization Toolbox]][https://github.com/yosinski/deep-visualization-toolbox]<ref>httphttps://arxiv.org/abs/1506.06579</ref><ref>http://yosinski.com/deepvis</ref> – Software tool for "probing" DNNs by feeding them image data and watching the reaction of every neuron, and for visualizing what a specific neuron "wants to see the most"

* [[LSTMVis]][http://lstm.seas.harvard.edu/] – A visual analysis tool for recurrent neural networks

* [[pastalog]][https://github.com/rewonc/pastalog] – Simple, realtime visualization of neural network training performance

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* [https://github.com/jtoy/awesome-tensorflow#libraries Awesome TensorFlow – Libraries]

* [http://deep-learning.sg.tn/index.php/2-non-categorise/5-popular-deep-learning-libraries Popular Deep Learning Libraries]

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[[Category:Data mining and machine learning software]]