Deep Learning Frameworks

• Apache SINGA
• Blocks
• BrainCore
• Brainstorm
• Caffe
• Chainer
• CNTK
• ConvNetJS
• DeepBeliefSDK
• DeepDetect
• Deeplearning4j (DL4J)
• DeepLearningKit
• DeepSpark
• DIGITS
• dp
• IDLF
• Keras
• Lasagne
• Leaf
• Marvin
• Mocha.jl
• MXNet
• neocortex.js
• Neon
• OpenNN
• Petuum
• Platoon
• Poseidon
• Purine
• TensorFlow
• Theano
• Torch
• VELES
• Benchmarks
• Papers
• Codes
• References

Apache SINGA

• project-website: http://singa.incubator.apache.org/
• github: https://github.com/apache/incubator-singa
• paper: http://www.comp.nus.edu.sg/~ooibc/singaopen-mm15.pdf

Blocks

Blocks: A Theano framework for building and training neural networks

• github: https://github.com/mila-udem/blocks

Blocks and Fuel: Frameworks for deep learning

• arXiv: http://arxiv.org/abs/1506.00619

BrainCore

BrainCore: The iOS and OS X neural network framework

https://github.com/aleph7/BrainCore

Brainstorm

Brainstorm: Fast, flexible and fun neural networks

• github: https://github.com/IDSIA/brainstorm

Caffe

Caffe: Convolutional Architecture for Fast Feature Embedding

• github: https://github.com/BVLC/caffe
• paper: http://arxiv.org/abs/1408.5093
• tutorial: http://tutorial.caffe.berkeleyvision.org/
• slides: http://vision.stanford.edu/teaching/cs231n/slides/caffe_tutorial.pdf
• slides: http://vision.princeton.edu/courses/COS598/2015sp/slides/Caffe/caffe_tutorial.pdf
• caffe-doc: http://caffe.berkeleyvision.org/doxygen/index.html
• tutorials(“CAFFE with CUDA”): http://pan.baidu.com/s/1i4kmpyH
• blog(“Convolution in Caffe: a memo”): https://github.com/Yangqing/caffe/wiki/Convolution-in-Caffe:-a-memo

Caffe on both Linux and Windows

• github: https://github.com/Microsoft/caffe

ApolloCaffe: a fork of Caffe that supports dynamic networks

• homepage: http://apollocaffe.com/
• github: http://github.com/Russell91/apollocaffe

fb-caffe-exts: Some handy utility libraries and tools for the Caffe deep learning framework

• github: https://github.com/facebook/fb-caffe-exts

Caffe-Android-Lib: Porting caffe to android platform

• github: https://github.com/sh1r0/caffe-android-lib

caffe-android-demo: An android caffe demo app exploiting caffe pre-trained ImageNet model for image classification

• github: https://github.com/sh1r0/caffe-android-demo

DIY Deep Learning for Vision: a Hands-On Tutorial with Caffe

• homepage: http://tutorial.caffe.berkeleyvision.org/

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OpenCV 3.0.0-dev: Load Caffe framework models

http://docs.opencv.org/master/d5/de7/tutorial_dnn_googlenet.html#gsc.tab=0

Chainer

Chainer: a neural network framework

• website: http://chainer.org/
• github: https://github.com/pfnet/chainer
• benchmark: http://chainer.readthedocs.org/en/latest/comparison.html

Introduction to Chainer: Neural Networks in Python

• blog: http://multithreaded.stitchfix.com/blog/2015/12/09/intro-to-chainer/
• github: https://github.com/stitchfix/Algorithms-Notebooks

CNTK

CNTK: Computational Network Toolkit

• github: https://github.com/Microsoft/CNTK
• book: http://research.microsoft.com/pubs/226641/CNTKBook-20160121.pdf
• tutorial: http://research.microsoft.com/en-us/um/people/dongyu/CNTK-Tutorial-NIPS2015.pdf

An Introduction to Computational Networks and the Computational Network Toolkit

http://research.microsoft.com/apps/pubs/?id=226641

ConvNetJS

ConvNetJS: Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser

• github: https://github.com/karpathy/convnetjs

DeepBeliefSDK

DeepBeliefSDK: The SDK for Jetpac’s iOS, Android, Linux, and OS X Deep Belief image recognition framework

• github: https://github.com/jetpacapp/DeepBeliefSDK
• demo: https://github.com/jetpacapp/Jetpac-Deep-Belief-Demo-App
• demo: https://github.com/jetpacapp/Jetpac-Deep-Belief-Learner-Demo-App

DeepDetect

DeepDetect: Open Source API & Deep Learning Server

• webiste: http://www.deepdetect.com/
• github: https://github.com/beniz/deepdetect

Deeplearning4j (DL4J)

Deeplearning4j: Deep Learning for Java

• website: http://deeplearning4j.org/

DeepLearningKit

DeepLearningKit: Open Source Deep Learning Framework for Apple’s tvOS, iOS and OS X

• homepage: http://deeplearningkit.org/
• github: https://github.com/DeepLearningKit/DeepLearningKit

Tutorial — Using DeepLearningKit with iOS for iPhone and iPad

https://medium.com/@atveit/tutorial-using-deeplearningkit-with-ios-for-iphone-and-ipad-de727679bae4#.1bvnhxhjo

DeepSpark

DeepSpark: Deeplearning framework running on Spark

• github: https://github.com/deepspark/deepspark
• homepage: http://deepspark.snu.ac.kr/
• arxiv: http://arxiv.org/abs/1602.08191

DIGITS

DIGITS: the Deep Learning GPU Training System

• github: https://github.com/NVIDIA/DIGITS

dp

dp: A deep learning library for streamlining research and development using the Torch7 distribution

• github: https://github.com/nicholas-leonard/dp
• manual: https://dp.readthedocs.org/en/latest/
• manual: https://github.com/nicholas-leonard/dp/blob/master/doc/index.md

IDLF

IDLF: The Intel® Deep Learning Framework

• website: https://01.org/zh/intel-deep-learning-framework?langredirect=1
• github: https://github.com/01org/idlf

Keras

Keras: Theano-based Deep Learning library

• github: https://github.com/fchollet/keras

Lasagne

Lasagne: Lightweight library to build and train neural networks in Theano

• github: https://github.com/Lasagne/Lasagne
• homepage: http://lasagne.readthedocs.org/en/latest/

Leaf

Leaf: The Hacker’s Machine Learning Engine

• homepage: http://autumnai.github.io/leaf/leaf/index.html
• github: https://github.com/autumnai/leaf

Marvin

Marvin: A minimalist GPU-only N-dimensional ConvNet framework

• website: http://marvin.is/
• github: https://github.com/PrincetonVision/marvin

Mocha.jl

Mocha.jl: Deep Learning for Julia

• website: http://devblogs.nvidia.com/parallelforall/mocha-jl-deep-learning-julia/
• github: https://github.com/pluskid/Mocha.jl

MXNet

MXNet

• github: https://github.com/dmlc/mxnet
• paper: https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/paper/mxnet-learningsys.pdf

a short introduction to mxnet design and implementation (chinese)

• github: https://github.com/dmlc/mxnet/blob/master/doc/overview_chn.md
• github-issues: https://github.com/dmlc/mxnet/issues/797

Deep learning for hackers with MXnet (1) GPU installation and MNIST

https://no2147483647.wordpress.com/2015/12/07/deep-learning-for-hackers-with-mxnet-1/

mxnet_Efficient, Flexible Deep Learning Framework

• slides: http://vdisk.weibo.com/s/z5dg0jVVHv2pn/1450157571

Deep Learning in a Single File for Smart Devices

https://mxnet.readthedocs.org/en/latest/tutorial/smart_device.html

用MXnet实战深度学习之一:安装GPU版mxnet并跑一个MNIST手写数字识别

http://phunter.farbox.com/post/mxnet-tutorial1

用MXnet实战深度学习之二:Neural art

http://phunter.farbox.com/post/mxnet-tutorial2

Programming Models and Systems Design for Deep Learning

• video: http://research.microsoft.com/apps/video/default.aspx?id=262396
• video: http://pan.baidu.com/s/1mgSnj64

Awesome MXNet

• intro: This page contains a curated list of awesome MXnet examples, tutorials and blogs.
• github: https://github.com/dmlc/mxnet/blob/master/example/README.md

Getting Started with MXNet

https://indico.io/blog/getting-started-with-mxnet/

neocortex.js

Run trained deep neural networks in the browser or node.js

• homepage: http://scienceai.github.io/neocortex/
• github: https://github.com/scienceai/neocortex

Neon

Neon: Nervana’s Python-based deep learning library

• website: http://neon.nervanasys.com/docs/latest/index.html
• github: https://github.com/NervanaSystems/neon

Tools to convert Caffe models to neon’s serialization format

• github: https://github.com/NervanaSystems/caffe2neon

OpenNN

OpenNN - Open Neural Networks Library

• homepage: http://opennn.net/
• github: https://github.com/artelnics/opennn

Petuum

Petuum: a distributed machine learning framework

• website: http://petuum.github.io/
• github: https://github.com/petuum/bosen

Platoon

Platoon: Multi-GPU mini-framework for Theano

• github: https://github.com/mila-udem/platoon

Poseidon

Poseidon: Distributed Deep Learning Framework on Petuum

• github: https://github.com/petuum/poseidon
• wiki: https://github.com/petuum/poseidon/wiki

Purine

Purine: A bi-graph based deep learning framework

• github: https://github.com/purine/purine2
• arXiv: http://arxiv.org/abs/1412.6249

TensorFlow

TensorFlow

• website: http://tensorflow.org/
• whitepaper: http://download.tensorflow.org/paper/whitepaper2015.pdf
• github: https://github.com/tensorflow/tensorflow
• github: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/distributed_runtime
• tutorial: http://tensorflow.org/tutorials
• tutorial: https://github.com/nlintz/TensorFlow-Tutorials
• stackoverflow: https://stackoverflow.com/questions/tagged/tensorflow
• benchmark: https://github.com/soumith/convnet-benchmarks/issues/66
• tutorial-zh: https://github.com/jikexueyuanwiki/tensorflow-zh

TensorDebugger(TDB): Interactive, node-by-node debugging and visualization for TensorFlow

• github: https://github.com/ericjang/tdb

ofxMSATensorFlow: OpenFrameworks addon for Google’s data-flow graph based numerical computation / machine intelligence library TensorFlow.

• github: https://github.com/memo/ofxMSATensorFlow

Installing TensorFlow on Raspberry Pi 3 (and probably 2 as well)

• github: https://github.com/samjabrahams/tensorflow-on-raspberry-pi

LearningTensorFlow.com: A beginners guide to a powerful framework. - homepge: http://learningtensorflow.com/index.html

TFLearn: Deep learning library featuring a higher-level API for TensorFlow

• github: https://github.com/tflearn/tflearn
• homepage: http://tflearn.org/

Theano

Theano

• website: http://deeplearning.net/software/theano/index.html
• github: https://github.com/Theano/Theano
• tutorial: https://github.com/Newmu/Theano-Tutorials

Configuring Theano For High Performance Deep Learning

http://www.johnwittenauer.net/configuring-theano-for-high-performance-deep-learning/

Theano: a short practical guide

• slides: http://folinoid.com/show/theano/

Torch

Torch

• website: http://torch.ch/
• github: https://github.com/torch/torch7
• cheatsheet: https://github.com/torch/torch7/wiki/Cheatsheet
• tutorials(“Getting started with Torch”): [http://torch.ch/docs/getting-started.html#](http://torch.ch/docs/getting-started.html#)

loadcaffe: Load Caffe networks in Torch7

• github: https://github.com/szagoruyko/loadcaffe

Applied Deep Learning for Computer Vision with Torch

• homepage: https://github.com/soumith/cvpr2015

VELES

VELES: Distributed platform for rapid Deep learning application development

• website: https://velesnet.ml/
• github: https://github.com/Samsung/veles
• workflow: https://velesnet.ml/forge/forge.html

Benchmarks

https://github.com/soumith/convnet-benchmarks

Papers

Comparative Study of Caffe, Neon, Theano, and Torch for Deep Learning

• arXiv: http://arxiv.org/abs/1511.06435

Codes

TensorFuse: Common interface for Theano, CGT, and TensorFlow

• github: https://github.com/dementrock/tensorfuse

References

Frameworks and Libraries for Deep Learning

http://creative-punch.net/2015/07/frameworks-and-libraries-for-deep-learning/

TensorFlow vs. Theano vs. Torch

https://github.com/zer0n/deepframeworks/blob/master/README.md

Evaluation of Deep Learning Toolkits

https://github.com/zer0n/deepframeworks/blob/master/README.md

Deep Machine Learning libraries and frameworks

https://medium.com/@abduljaleel/deep-machine-learning-libraries-and-frameworks-5fdf2bb6bfbe#.q1mhj7c36

Torch vs Theano

• blog: http://fastml.com/torch-vs-theano/

Deep Learning Software: NVIDIA Deep Learning SDK

https://developer.nvidia.com/deep-learning-software

A comparison of deep learning frameworks

• intro: Theano/CGT/Torch/MXNet
• gist: https://gist.github.com/bartvm/69adf7aad100d58831b0
• webo: http://weibo.com/p/1001603946281180481229

TensorFlow Meets Microsoft’s CNTK

• blog: http://esciencegroup.com/2016/02/08/tensorflow-meets-microsofts-cntk/

Is there a case for still using Torch, Theano, Brainstorm, MXNET and not switching to TensorFlow?

• reddit: [https://www.reddit.com/r/MachineLearning/comments/47qh90/is_there_a_case_for_still_using_torch_theano/][https://www.reddit.com/r/MachineLearning/comments/47qh90/is_there_a_case_for_still_using_torch_theano/]

DL4J vs. Torch vs. Theano vs. Caffe vs. TensorFlow

http://deeplearning4j.org/compare-dl4j-torch7-pylearn.html