Deep Learning Frameworks

Amazon DSSTNE

Amazon DSSTNE: Deep Scalable Sparse Tensor Network Engine

• intro: Deep Scalable Sparse Tensor Network Engine (DSSTNE) is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models
• github: https://github.com/amznlabs/amazon-dsstne

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
• paper: http://www.comp.nus.edu.sg/~ooibc/singa-tomm.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

OpenCL Caffe

• intro: an experimental, community-maintained branch
• github: https://github.com/BVLC/caffe/tree/opencl

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

• intro: fb-caffe-exts is a collection of extensions developed at FB while using Caffe in (mainly) production scenarios.
• 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

Caffe.js: Run Caffe models in the browser using ConvNetJS

• github: https://github.com/chaosmail/caffejs/
• demo: http://chaosmail.github.io/caffejs/models.html

Intel Caffe

• intro: This fork of BVLC/Caffe is dedicated to improving performance of this deep learning framework when running on CPU, in particular Intel® Xeon processors (HSW+) and Intel® Xeon Phi processors
• github https://github.com/intel/caffe

NVIDIA Caffe

https://github.com/NVIDIA/caffe

Mini-Caffe

• intro: Minimal runtime core of Caffe, Forward only, GPU support and Memory efficiency.
• github: https://github.com/luoyetx/mini-caffe

Caffe on Mobile Devices

• intro: Optimized (for size and speed) Caffe lib for iOS and Android with demo APP.
• github: https://github.com/solrex/caffe-mobile

CaffeOnACL

• intro: Using ARM Compute Library (NEON+GPU) to speed up caffe; Providing utilities to debug, profile and tune application performance
• github: https://github.com/OAID/caffeOnACL

Multi-GPU / MPI Caffe

Caffe with OpenMPI-based Multi-GPU support

• intro: A fork of Caffe with OpenMPI-based Multi-GPU (mainly data parallel) support for action recognition and more.
• github: https://github.com/yjxiong/caffe/tree/mem

mpi-caffe: Model-distributed Deep Learning with Caffe and MPI

• project page: https://computing.ece.vt.edu/~steflee/mpi-caffe.html
• github: https://github.com/steflee/mpi-caffe

Caffe-MPI for Deep Learning

• github: https://github.com/Caffe-MPI/Caffe-MPI.github.io
• slides: http://mug.mvapich.cse.ohio-state.edu/static/media/mug/presentations/2016/Caffe-MPI_A_Parallel_Framework_on_the_GPU_Clusters.pdf

Caffe Utils

Caffe-model

• intro: Python script to generate prototxt on Caffe, specially the inception_v3\inception_v4\inception_resnet\fractalnet
• github: https://github.com/soeaver/caffe-model

Caffe2

Caffe2: A New Lightweight, Modular, and Scalable Deep Learning Framework

• intro: Caffe2 is a deep learning framework made with expression, speed, and modularity in mind. It is an experimental refactoring of Caffe, and allows a more flexible way to organize computation.
• homepage: https://caffe2.ai/
• github https://github.com/caffe2/caffe2
• github https://github.com/Yangqing/caffe2
• model zoo: https://caffe2.ai/docs/zoo.html
• models: https://github.com/caffe2/models

CDNN2

CDNN2 - CEVA Deep Neural Network Software Framework

• intro: Accelerating the development of Artificial Intelligence and its deployment in Low-Power Embedded Systems

• homepage: http://launch.ceva-dsp.com/cdnn2/

• blog: http://www.tomshardware.com/news/ceva-cdnn2-tensorflow-embedded-systems,32158.html

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

• homepage: http://deeplearning4j.org/
• github: https://github.com/deeplearning4j/deeplearning4j

Deeplearning4j images for cuda and hadoop.

• github: https://github.com/deeplearning4j/docker

Deeplearning4J Examples

• intro: Deeplearning4j Examples (DL4J, DL4J Spark, DataVec)
• github: https://github.com/deeplearning4j/dl4j-examples

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

• homepage: https://developer.nvidia.com/digits
• 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

Dragon

Dragon: A Computation Graph Virtual Machine Based Deep Learning Framework

• arxiv: https://arxiv.org/abs/1707.08265
• github: https://github.com/neopenx/Dragon

DyNet

**DyNet: The Dynamic Neural Network Toolkit **

• paper: https://arxiv.org/abs/1701.03980
• github: https://github.com/clab/dynet

DyNet Benchmarks

• github: https://github.com/neulab/dynet-benchmark

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: Deep Learning library for Theano and TensorFlow

• github: https://github.com/fchollet/keras
• blog: http://blog.keras.io/introducing-keras-10.html
• docs: http://keras.io/getting-started/functional-api-guide/

MarcBS/keras fork

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

Hera: Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.

• github: https://github.com/jakebian/hera

Installing Keras for deep learning

• blog: http://www.pyimagesearch.com/2016/07/18/installing-keras-for-deep-learning/

Keras Applications - deep learning models that are made available alongside pre-trained weights

https://keras.io/applications/

Keras resources: Directory of tutorials and open-source code repositories for working with Keras, the Python deep learning library

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

Keras.js: Run trained Keras models in the browser, with GPU support

• homepage: https://transcranial.github.io/keras-js/
• github: https://github.com/transcranial/keras-js

keras2cpp

• intro: This is a bunch of code to port Keras neural network model into pure C++.
• github: https://github.com/pplonski/keras2cpp

keras-cn: Chinese keras documents with more examples, explanations and tips.

• github: https://github.com/MoyanZitto/keras-cn

Kerasify: Small library for running Keras models from a C++ application

https://github.com/moof2k/kerasify

Knet

Knet: Koç University deep learning framework

• intro: Knet (pronounced “kay-net”) is the Koç University deep learning framework implemented in Julia by Deniz Yuret and collaborators.
• github: https://github.com/denizyuret/Knet.jl
• doc: https://knet.readthedocs.org/en/latest/

Lasagne

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

• github: https://github.com/Lasagne/Lasagne
• docs: 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
• homepage: http://autumnai.com/leaf/book/leaf.html
• homepage(“The Hacker’s Machine Intelligence Platform”): http://autumnai.com/

LightNet

LightNet: A Versatile, Standalone and Matlab-based Environment for Deep Learning

• homepage: http://www.umiacs.umd.edu/~yzyang/LightNet/
• github: https://github.com/yechengxi/lightnet

MatConvNet

MatConvNet: CNNs for MATLAB

• homepage: http://www.vlfeat.org/matconvnet/
• github: https://github.com/vlfeat/matconvnet

Marvin

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

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

MatConvNet: CNNs for MATLAB

• homepage: http://www.vlfeat.org/matconvnet/
• pretianed models: http://www.vlfeat.org/matconvnet/pretrained/

Mocha.jl

Mocha.jl: Deep Learning for Julia

• homepage: 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

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

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

MXNet Model Gallery: Pre-trained Models of DMLC Project

• github: https://github.com/dmlc/mxnet-model-gallery

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

Use Caffe operator in MXNet

• blog: http://dmlc.ml/mxnet/2016/07/29/use-caffe-operator-in-mxnet.html**

Deep Learning in a Single File for Smart Devices

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

MXNet Pascal Titan X benchmark

• blog: http://dmlc.ml/mxnet/2016/08/03/mxnet-titanx-benchmark.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/

gtc_tutorial: MXNet Tutorial for NVidia GTC 2016

• report: http://on-demand.gputechconf.com/gtc/2016/video/S6853.html
• tutorial: http://on-demand.gputechconf.com/gtc/2016/video/L6143.html
• video: http://pan.baidu.com/s/1eS58Gue
• github: https://github.com/dmlc/mxnet-gtc-tutorial

MXNET Dependency Engine

• blog: http://yuyang0.github.io/articles/mxnet-engine.html

MXNET是这样压榨深度学习的内存消耗的

• doc: https://github.com/dmlc/mxnet/blob/master/docs/zh/note_memory.md

WhatsThis-iOS: MXNet WhatThis Example for iOS

• github: https://github.com/pppoe/WhatsThis-iOS

MXNET-MPI: Embedding MPI parallelism in Parameter Server Task Model for scaling Deep Learning

• intro: IBM T J Watson Research Center
• arxiv: https://arxiv.org/abs/1801.03855

ncnn

• intro: ncnn is a high-performance neural network inference framework optimized for the mobile platform
• github: https://github.com/Tencent/ncnn

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
• website: https://www.nervanasys.com/learn/

Tools to convert Caffe models to neon’s serialization format

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

Nervana’s Deep Learning Course

• homepage: https://www.nervanasys.com/deep-learning-tutorials/
• github: https://github.com/NervanaSystems/neon_course

NNabla

NNabla - Neural Network Libraries by Sony

• intro: NNabla - Neural Network Libraries NNabla is a deep learning framework that is intended to be used for research, development and production. We aim it running everywhere like desktop PCs, HPC clusters, embedded devices and production servers.
• homepage: https://nnabla.org/
• github: https://github.com/sony/nnabla

OpenDeep

OpenDeep: a fully modular & extensible deep learning framework in Python

• intro: Modular & extensible deep learning framework built on Theano
• homepage: http://www.opendeep.org/
• github: https://github.com/vitruvianscience/opendeep

OpenNN

OpenNN - Open Neural Networks Library

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

Paddle

PaddlePaddle: PArallel Distributed Deep LEarning

• homepage: http://www.paddlepaddle.org/
• github: https://github.com/baidu/Paddle
• installation: http://www.paddlepaddle.org/doc/build/

基于Spark的异构分布式深度学习平台

http://geek.csdn.net/news/detail/58867

Petuum

Petuum: a distributed machine learning framework

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

PlaidML

PlaidML: A framework for making deep learning work everywhere

• homepage: http://vertex.ai/
• github: https://github.com/plaidml/plaidml

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

PyTorch

PyTorch

• github: https://github.com/pytorch/pytorch

Datasets, Transforms and Models specific to Computer Vision

https://github.com/pytorch/vision/

Convert torch to pytorch

https://github.com/clcarwin/convert_torch_to_pytorch

TensorFlow

TensorFlow

• website: http://tensorflow.org/
• 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

Benchmarks

• intro: A selection of image classification models were tested across multiple platforms to create a point of reference for the TensorFlow community
• homepage: https://www.tensorflow.org/performance/benchmarks

TensorDebugger (TDB)

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

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

• homepage: http://tflearn.org/
• github: https://github.com/tflearn/tflearn
• examples: https://github.com/tflearn/tflearn/blob/0.1.0/examples/README.md

TensorFlow on Spark

• github: https://github.com/adatao/tensorspark

TensorBoard

TensorFlow.jl: A Julia wrapper for the TensorFlow Python library

• github: https://github.com/benmoran/TensorFlow.jl

TensorLayer: Deep learning and Reinforcement learning library for TensorFlow

• github: https://github.com/zsdonghao/tensorlayer
• docs: http://tensorlayer.readthedocs.io/en/latest/

OpenCL support for TensorFlow

• github: https://github.com/benoitsteiner/tensorflow-opencl

Pretty Tensor: Fluent Networks in TensorFlow

• github: https://github.com/google/prettytensor
• docs: https://github.com/google/prettytensor/blob/master/docs/pretty_tensor_top_level.md
• tutorials: https://github.com/google/prettytensor/tree/master/prettytensor/tutorial

Rust language bindings for TensorFlow

• github: https://github.com/tensorflow/rust

TensorFlow Ecosystem: Integration of TensorFlow with other open-source frameworks

• github: https://github.com/tensorflow/ecosystem

Caffe to TensorFlow

• intro: Convert Caffe models to TensorFlow.
• github: https://github.com/ethereon/caffe-tensorflow

TensorFlow Mobile

https://www.tensorflow.org/mobile/

Papers

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

• arxiv: http://arxiv.org/abs/1603.04467
• whitepaper: http://download.tensorflow.org/paper/whitepaper2015.pdf

TensorFlow: A system for large-scale machine learning

• arxiv: http://arxiv.org/abs/1605.08695

TensorFlow Distributions

https://arxiv.org/abs/1711.10604

Tutorials

TensorFlow 官方文档中文版

• tutorial-zh: https://github.com/jikexueyuanwiki/tensorflow-zh
• homepage: http://wiki.jikexueyuan.com/project/tensorflow-zh/

Theano

Theano

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

Theano-Tutorials: Bare bones introduction to machine learning from linear regression to convolutional neural networks using Theano

• github: https://github.com/Newmu/Theano-Tutorials

Theano: A Python framework for fast computation of mathematical expressions

• arxiv: http://arxiv.org/abs/1605.02688

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/

Ian Goodfellow’s Tutorials on Theano

• slides: http://pan.baidu.com/s/1slbzhF3#path=%252F%25E6%2588%2591%25E7%259A%2584%25E5%2588%2586%25E4%25BA%25AB%252F201604%252FIan%2520Goodfellow’s%2520Tutorials%2520on%2520Theano
• github(“theano_exercises”): https://github.com/goodfeli/theano_exercises

Plato: A library built on top of Theano

• github: https://github.com/petered/plato
• tutorial: https://rawgit.com/petered/plato/master/plato_tutorial.html

Theano Windows Install Guide

• github: https://github.com/mrakgr/Tutorials/blob/master/theano_install.md

Theano-MPI: a Theano-based Distributed Training Framework

• arxiv: https://arxiv.org/abs/1605.08325
• github: https://github.com/uoguelph-mlrg/Theano-MPI

tiny-dnn (tiny-cnn)

tiny-dnn: A header only, dependency-free deep learning framework in C++11

• inrtro: tiny-dnn is a C++11 implementation of deep learning. It is suitable for deep learning on limited computational resource, embedded systems and IoT devices.
• github: https://github.com/tiny-dnn/tiny-dnn
• github: https://github.com/nyanp/tiny-cnn

Deep learning with C++ - an introduction to tiny-dnn

• slides: http://www.slideshare.net/ssuser756ec5/deep-learning-with-c-an-introduction-to-tinydnn

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

pytorch: Python wrappers for torch and lua

• github: https://github.com/hughperkins/pytorch

Torch Toolbox: A collection of snippets and libraries for Torch

• github: https://github.com/e-lab/torch-toolbox

cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL

• arxiv: http://arxiv.org/abs/1606.04884
• github: https://github.com/hughperkins/cltorch

Torchnet: An Open-Source Platform for (Deep) Learning Research

• paper: https://lvdmaaten.github.io/publications/papers/Torchnet_2016.pdf
• github: https://github.com/torchnet/torchnet

THFFmpeg: Torch bindings for FFmpeg (reading videos only)

• github: https://github.com/MichaelMathieu/THFFmpeg

caffegraph: Load Caffe networks in Torch7 using nngraph

• github: https://github.com/nhynes/caffegraph

Optimized-Torch: Intel Torch is dedicated to improving Torch performance when running on CPU

• intro: Intel Torch gets 4.66x speedup using the convnet-benchmarks which includes AlexNet,VGG-E,GoogLenet,ResidualNet
• github: https://github.com/xhzhao/optimized-torch
• benchmark: https://github.com/xhzhao/Optimized-Torch-benchmark

Torch Video Tutorials

• github: https://github.com/Atcold/torch-Video-Tutorials

Torch in Action

• github: https://github.com/nicholas-leonard/torch-in-action

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

WebDNN

WebDNN: Fastest DNN Execution Framework on Web Browser

• homepage: https://mil-tokyo.github.io/webdnn/
• github: https://github.com/mil-tokyo/webdnn

Yann

Yann: Yet Another Neural Network Toolbox

• intro: It is a toolbox for building and learning convolutional neural networks, built on top of theano
• github: https://github.com/ragavvenkatesan/yann
• docs: http://yann.readthedocs.io/en/master/

Benchmarks

Easy benchmarking of all publicly accessible implementations of convnets

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

Stanford DAWN Deep Learning Benchmark (DAWNBench) - An End-to-End Deep Learning Benchmark and Competition

http://dawn.cs.stanford.edu/benchmark/index.html

Tutorials

Deep Learning Implementations and Frameworks (DLIF)

• tutorial: https://sites.google.com/site/dliftutorial/
• github: https://github.com/delta2323/DLIF-tutorial

Papers

Comparative Study of Deep Learning Software Frameworks

• intro: Caffe / Neon / TensorFlow / Theano / Torch
• arxiv: http://arxiv.org/abs/1511.06435
• github: https://github.com/DL-Benchmarks/DL-Benchmarks

Benchmarking State-of-the-Art Deep Learning Software Tools

• intro: Caffe, CNTK, MXNet, TensorFlow, and Torch
• project page: http://dlbench.comp.hkbu.edu.hk/
• arxiv: http://arxiv.org/abs/1608.07249

Projects

TensorFuse: Common interface for Theano, CGT, and TensorFlow

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

DeepRosetta: An universal deep learning models conversor

• github: https://github.com/edgarriba/DeepRosetta

Deep Learning Model Convertors

https://github.com/ysh329/deep-learning-model-convertor

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

Popular Deep Learning Libraries

• blog: http://machinelearningmastery.com/popular-deep-learning-libraries/

The simple example of Theano and Lasagne super power

https://grzegorzgwardys.wordpress.com/2016/05/15/the-simple-example-of-theano-and-lasagne-super-power/

Comparison of deep learning software

• wiki: https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software

A Look at Popular Machine Learning Frameworks

• blog: http://redmonk.com/fryan/2016/06/06/a-look-at-popular-machine-learning-frameworks/

5 Deep Learning Projects You Can No Longer Overlook

• keywords: Leaf / tiny-cnn / Layered / Brain / neon
• blog: http://www.kdnuggets.com/2016/07/five-deep-learning-projects-cant-overlook.html

Comparison of Deep Learning Libraries After Years of Use

• intro: Torch / MxNet / Theano / Caffe
• blog:http://www.erogol.com/comparison-deep-learning-libraries-years-use/

Deep Learning Part 1: Comparison of Symbolic Deep Learning Frameworks

• intro: Theano / TensorFlow / MXNET
• blog: http://blog.revolutionanalytics.com/2016/08/deep-learning-part-1.html

Deep Learning Frameworks Compared

• youtube: https://www.youtube.com/watch?v=MDP9FfsNx60
• github: https://github.com/llSourcell/tensorflow_vs_theano

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

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

Deep Learning frameworks: a review before finishing 2016

https://medium.com/@ricardo.guerrero/deep-learning-frameworks-a-review-before-finishing-2016-5b3ab4010b06#.a6fdrqssl

The Anatomy of Deep Learning Frameworks

https://medium.com/@gokul_uf/the-anatomy-of-deep-learning-frameworks-46e2a7af5e47

Python Deep Learning Frameworks Reviewed

https://indico.io/blog/python-deep-learning-frameworks-reviewed/

Apple’s deep learning frameworks: BNNS vs. Metal CNN

http://machinethink.net/blog/apple-deep-learning-bnns-versus-metal-cnn/