偶然在github上看到Awesome Deep Learning项目，故分享一下。其中涉及深度学习的免费在线书籍、课程、视频及讲义、论文、教程、网站、数据集、框架和其他资源，包罗万象，非常值得学习。
其中研究人员部分篇幅所限本文未整理进来。另外上面的GIF录制于MIT自动驾驶课程（MIT 6.S094: Deep Learning for Self-Driving Cars）
PS：github上取名“awesome”的一般都非常牛逼，此项目亦然！
以下整理至：Awesome Deep Learning。
Awesome Deep Learning
Table of Contents
Free Online Books
Courses
Videos and Lectures
Papers
Tutorials
WebSites
Datasets
Frameworks
Miscellaneous
Free Online Books
Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville (05/07/2015)
Neural Networks and Deep Learning by Michael Nielsen (Dec 2014)
Deep Learning by Microsoft Research (2013)
Deep Learning Tutorial by LISA lab, University of Montreal (Jan 6 2015)
neuraltalk by Andrej Karpathy : numpy-based RNN/LSTM implementation
An introduction to genetic algorithms
Artificial Intelligence: A Modern Approach
Deep Learning in Neural Networks: An Overview
Courses
Machine Learning - Stanford by Andrew Ng in Coursera (2010-2014)
Machine Learning - Caltech by Yaser Abu-Mostafa (2012-2014)
Machine Learning - Carnegie Mellon by Tom Mitchell (Spring 2011)
Neural Networks for Machine Learning by Geoffrey Hinton in Coursera (2012)
Neural networks class by Hugo Larochelle from Université de Sherbrooke (2013)
Deep Learning Course by CILVR lab @ NYU (2014)
A.I - Berkeley by Dan Klein and Pieter Abbeel (2013)
A.I - MIT by Patrick Henry Winston (2010)
Vision and learning - computers and brains by Shimon Ullman, Tomaso Poggio, Ethan Meyers @ MIT (2013)
Convolutional Neural Networks for Visual Recognition - Stanford by Fei-Fei Li, Andrej Karpathy (2015)
Convolutional Neural Networks for Visual Recognition - Stanford by Fei-Fei Li, Andrej Karpathy (2016)
Deep Learning for Natural Language Processing - Stanford
Neural Networks - usherbrooke
Machine Learning - Oxford(2014-2015)
Deep Learning - Nvidia(2015)
Graduate Summer School: Deep Learning, Feature Learning by Geoffrey Hinton, Yoshua Bengio, Yann LeCun, Andrew Ng, Nando de Freitas and several others @ IPAM, UCLA (2012)
Deep Learning - Udacity/Google by Vincent Vanhoucke and Arpan Chakraborty (2016)
Deep Learning - UWaterloo by Prof. Ali Ghodsi at University of Waterloo (2015)
Statistical Machine Learning - CMU by Prof. Larry Wasserman
Deep Learning Course by Yann LeCun (2016)
Bay area DL school by Andrew Ng, Yoshua Bengio, Samy Bengio, Andrej Karpathy, Richard Socher, Hugo Larochelle and many others @ Stanford, CA (2016)
Designing, Visualizing and Understanding Deep Neural Networks-UC Berkeley
UVA Deep Learning Course MSc in Artificial Intelligence for the University of Amsterdam.
MIT 6.S094: Deep Learning for Self-Driving Cars
MIT 6.S191: Introduction to Deep Learning
Berkeley CS 294: Deep Reinforcement Learning
Keras in Motion video course
Practical Deep Learning For Coders by Jeremy Howard - Fast.ai
Videos and Lectures
How To Create A Mind By Ray Kurzweil
Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng
Recent Developments in Deep Learning By Geoff Hinton
The Unreasonable Effectiveness of Deep Learning by Yann LeCun
Deep Learning of Representations by Yoshua bengio
Principles of Hierarchical Temporal Memory by Jeff Hawkins
Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab by Adam Coates
Making Sense of the World with Deep Learning By Adam Coates
Demystifying Unsupervised Feature Learning By Adam Coates
Visual Perception with Deep Learning By Yann LeCun
The Next Generation of Neural Networks By Geoffrey Hinton at GoogleTechTalks
The wonderful and terrifying implications of computers that can learn By Jeremy Howard at TEDxBrussels
Unsupervised Deep Learning - Stanford by Andrew Ng in Stanford (2011)
Natural Language Processing By Chris Manning in Stanford
A beginners Guide to Deep Neural Networks By Natalie Hammel and Lorraine Yurshansky
Deep Learning: Intelligence from Big Data by Steve Jurvetson (and panel) at VLAB in Stanford.
Introduction to Artificial Neural Networks and Deep Learning by Leo Isikdogan at Motorola Mobility HQ
NIPS 2016 lecture and workshop videos- NIPS 2016
Papers
You can also find the most cited deep learning papers from here
ImageNet Classification with Deep Convolutional Neural Networks
Using Very Deep Autoencoders for Content Based Image Retrieval
Learning Deep Architectures for AI
CMU’s list of papers
Neural Networks for Named Entity Recognitionzip
Training tricks by YB
Geoff Hinton's reading list (all papers)
Supervised Sequence Labelling with Recurrent Neural Networks
Statistical Language Models based on Neural Networks
Training Recurrent Neural Networks
Recursive Deep Learning for Natural Language Processing and Computer Vision
Bi-directional RNN
LSTM
GRU - Gated Recurrent Unit
GFRNN..
LSTM: A Search Space Odyssey
A Critical Review of Recurrent Neural Networks for Sequence Learning
Visualizing and Understanding Recurrent Networks
Wojciech Zaremba, Ilya Sutskever, An Empirical Exploration of Recurrent Network Architectures
Recurrent Neural Network based Language Model
Extensions of Recurrent Neural Network Language Model
Recurrent Neural Network based Language Modeling in Meeting Recognition
Deep Neural Networks for Acoustic Modeling in Speech Recognition
Speech Recognition with Deep Recurrent Neural Networks
Reinforcement Learning Neural Turing Machines
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Google - Sequence to Sequence Learning with Neural Networks
Memory Networks
Policy Learning with Continuous Memory States for Partially Observed Robotic Control
Microsoft - Jointly Modeling Embedding and Translation to Bridge Video and Language
Neural Turing Machines
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Mastering the Game of Go with Deep Neural Networks and Tree Search
Batch Normalization
Residual Learning
Image-to-Image Translation with Conditional Adversarial Networks
Berkeley AI Research (BAIR) Laboratory
MobileNets by Google
Cross Audio-Visual Recognition in the Wild Using Deep Learning
Tutorials
UFLDL Tutorial 1
UFLDL Tutorial 2
Deep Learning for NLP (without Magic)
A Deep Learning Tutorial: From Perceptrons to Deep Networks
Deep Learning from the Bottom up
Theano Tutorial
Neural Networks for Matlab
Using convolutional neural nets to detect facial keypoints tutorial
Torch7 Tutorials
The Best Machine Learning Tutorials On The Web
VGG Convolutional Neural Networks Practical
TensorFlow tutorials
More TensorFlow tutorials
TensorFlow Python Notebooks
Keras and Lasagne Deep Learning Tutorials
Classification on raw time series in TensorFlow with a LSTM RNN
Using convolutional neural nets to detect facial keypoints tutorial
TensorFlow-World
WebSites
deeplearning.net
deeplearning.stanford.edu
nlp.stanford.edu
ai-junkie.com
cs.brown.edu/research/ai
eecs.umich.edu/ai
cs.utexas.edu/users/ai-lab
cs.washington.edu/research/ai
aiai.ed.ac.uk
www-aig.jpl.nasa.gov
csail.mit.edu
cgi.cse.unsw.edu.au/~aishare
cs.rochester.edu/research/ai
ai.sri.com
isi.edu/AI/isd.htm
nrl.navy.mil/itd/aic
hips.seas.harvard.edu
AI Weekly
stat.ucla.edu
deeplearning.cs.toronto.edu
jeffdonahue.com/lrcn/
visualqa.org
www.mpi-inf.mpg.de/departments/computer-vision...
Deep Learning News
Machine Learning is Fun! Adam Geitgey's Blog
Datasets
MNISTHandwritten digits
Google House Numbersfrom street view
CIFAR-10 and CIFAR-100
IMAGENET
Tiny Images80 Million tiny images6.
Flickr Data100 Million Yahoo dataset
Berkeley Segmentation Dataset 500
UC Irvine Machine Learning Repository
Flickr 8k
Flickr 30k
Microsoft COCO
VQA
Image QA
AT&T Laboratories Cambridge face database
AVHRR Pathfinder
Air Freight- The Air Freight data set is a ray-traced image sequence along with ground truth segmentation based on textural characteristics. (455 images + GT, each 160x120 pixels). (Formats: PNG)
Amsterdam Library of Object Images- ALOI is a color image collection of one-thousand small objects, recorded for scientific purposes. In order to capture the sensory variation in object recordings, we systematically varied viewing angle, illumination angle, and illumination color for each object, and additionally captured wide-baseline stereo images. We recorded over a hundred images of each object, yielding a total of 110,250 images for the collection. (Formats: png)
Annotated face, hand, cardiac & meat images- Most images & annotations are supplemented by various ASM/AAM analyses using the AAM-API. (Formats: bmp,asf)
Image Analysis and Computer Graphics
Brown University Stimuli- A variety of datasets including geons, objects, and "greebles". Good for testing recognition algorithms. (Formats: pict)
CAVIAR video sequences of mall and public space behavior- 90K video frames in 90 sequences of various human activities, with XML ground truth of detection and behavior classification (Formats: MPEG2 & JPEG)
Machine Vision Unit
CCITT Fax standard images- 8 images (Formats: gif)
CMU CIL's Stereo Data with Ground Truth- 3 sets of 11 images, including color tiff images with spectroradiometry (Formats: gif, tiff)
CMU PIE Database- A database of 41,368 face images of 68 people captured under 13 poses, 43 illuminations conditions, and with 4 different expressions.
CMU VASC Image Database- Images, sequences, stereo pairs (thousands of images) (Formats: Sun Rasterimage)
Caltech Image Database- about 20 images - mostly top-down views of small objects and toys. (Formats: GIF)
Columbia-Utrecht Reflectance and Texture Database- Texture and reflectance measurements for over 60 samples of 3D texture, observed with over 200 different combinations of viewing and illumination directions. (Formats: bmp)
Computational Colour Constancy Data- A dataset oriented towards computational color constancy, but useful for computer vision in general. It includes synthetic data, camera sensor data, and over 700 images. (Formats: tiff)
Computational Vision Lab
Content-based image retrieval database- 11 sets of color images for testing algorithms for content-based retrieval. Most sets have a description file with names of objects in each image. (Formats: jpg)
Efficient Content-based Retrieval Group
Densely Sampled View Spheres- Densely sampled view spheres - upper half of the view sphere of two toy objects with 2500 images each. (Formats: tiff)
Computer Science VII (Graphical Systems)
Digital Embryos- Digital embryos are novel objects which may be used to develop and test object recognition systems. They have an organic appearance. (Formats: various formats are available on request)
Univerity of Minnesota Vision Lab
El Salvador Atlas of Gastrointestinal VideoEndoscopy- Images and Videos of his-res of studies taken from Gastrointestinal Video endoscopy. (Formats: jpg, mpg, gif)
FG-NET Facial Aging Database- Database contains 1002 face images showing subjects at different ages. (Formats: jpg)
FVC2000 Fingerprint Databases- FVC2000 is the First International Competition for Fingerprint Verification Algorithms. Four fingerprint databases constitute the FVC2000 benchmark (3520 fingerprints in all).
Biometric Systems Lab- University of Bologna
Face and Gesture images and image sequences- Several image datasets of faces and gestures that are ground truth annotated for benchmarking
German Fingerspelling Database- The database contains 35 gestures and consists of 1400 image sequences that contain gestures of 20 different persons recorded under non-uniform daylight lighting conditions. (Formats: mpg,jpg)
Language Processing and Pattern Recognition
Groningen Natural Image Database- 4000+ 1536x1024 (16 bit) calibrated outdoor images (Formats: homebrew)
ICG Testhouse sequence- 2 turntable sequences from ifferent viewing heights, 36 images each, resolution 1000x750, color (Formats: PPM)
Institute of Computer Graphics and Vision
IEN Image Library- 1000+ images, mostly outdoor sequences (Formats: raw, ppm)
INRIA's Syntim images database- 15 color image of simple objects (Formats: gif)
INRIA
INRIA's Syntim stereo databases- 34 calibrated color stereo pairs (Formats: gif)
Image Analysis Laboratory- Images obtained from a variety of imaging modalities -- raw CFA images, range images and a host of "medical images". (Formats: homebrew)
Image Analysis Laboratory
Image Database- An image database including some textures
JAFFE Facial Expression Image Database- The JAFFE database consists of 213 images of Japanese female subjects posing 6 basic facial expressions as well as a neutral pose. Ratings on emotion adjectives are also available, free of charge, for research purposes. (Formats: TIFF Grayscale images.)
ATR Research, Kyoto, Japan
JISCT Stereo Evaluation - 44 image pairs. These data have been used in an evaluation of stereo analysis, as described in the April 1993 ARPA Image Understanding Workshop paper ``The JISCT Stereo Evaluation'' by R.C.Bolles, H.H.Baker, and M.J.Hannah, 263--274 (Formats: SSI)
MIT Vision Texture- Image archive (100+ images) (Formats: ppm)
MIT face images and more - hundreds of images (Formats: homebrew)
Machine Vision- Images from the textbook by Jain, Kasturi, Schunck (20+ images) (Formats: GIF TIFF)
Mammography Image Databases- 100 or more images of mammograms with ground truth. Additional images available by request, and links to several other mammography databases are provided. (Formats: homebrew)
ftp://ftp.cps.msu.edu/pub/prip- many images (Formats: unknown)
Middlebury Stereo Data Sets with Ground Truth- Six multi-frame stereo data sets of scenes containing planar regions. Each data set contains 9 color images and subpixel-accuracy ground-truth data. (Formats: ppm)
Middlebury Stereo Vision Research Page- Middlebury College
Modis Airborne simulator, Gallery and data set- High Altitude Imagery from around the world for environmental modeling in support of NASA EOS program (Formats: JPG and HDF)
NIST Fingerprint and handwriting - datasets - thousands of images (Formats: unknown)
NIST Fingerprint data - compressed multipart uuencoded tar file
NLM HyperDoc Visible Human Project- Color, CAT and MRI image samples - over 30 images (Formats: jpeg)
National Design Repository- Over 55,000 3D CAD and solid models of (mostly) mechanical/machined engineerign designs. (Formats: gif,vrml,wrl,stp,sat)
Geometric & Intelligent Computing Laboratory
OSU (MSU) 3D Object Model Database- several sets of 3D object models collected over several years to use in object recognition research (Formats: homebrew, vrml)
OSU (MSU/WSU) Range Image Database- Hundreds of real and synthetic images (Formats: gif, homebrew)
OSU/SAMPL Database: Range Images, 3D Models, Stills, Motion Sequences- Over 1000 range images, 3D object models, still images and motion sequences (Formats: gif, ppm, vrml, homebrew)
Signal Analysis and Machine Perception Laboratory
Otago Optical Flow Evaluation Sequences- Synthetic and real sequences with machine-readable ground truth optical flow fields, plus tools to generate ground truth for new sequences. (Formats: ppm,tif,homebrew)
Vision Research Group
ftp://ftp.limsi.fr/pub/quenot/opflow/testdata/piv/- Real and synthetic image sequences used for testing a Particle Image Velocimetry application. These images may be used for the test of optical flow and image matching algorithms. (Formats: pgm (raw))
LIMSI-CNRS/CHM/IMM/vision
LIMSI-CNRS
Photometric 3D Surface Texture Database- This is the first 3D texture database which provides both full real surface rotations and registered photometric stereo data (30 textures, 1680 images). (Formats: TIFF)
SEQUENCES FOR OPTICAL FLOW ANALYSIS (SOFA)- 9 synthetic sequences designed for testing motion analysis applications, including full ground truth of motion and camera parameters. (Formats: gif)
Computer Vision Group
Sequences for Flow Based Reconstruction- synthetic sequence for testing structure from motion algorithms (Formats: pgm)
Stereo Images with Ground Truth Disparity and Occlusion- a small set of synthetic images of a hallway with varying amounts of noise added. Use these images to benchmark your stereo algorithm. (Formats: raw, viff (khoros), or tiff)
Stuttgart Range Image Database- A collection of synthetic range images taken from high-resolution polygonal models available on the web (Formats: homebrew)
Department Image Understanding
The AR Face Database- Contains over 4,000 color images corresponding to 126 people's faces (70 men and 56 women). Frontal views with variations in facial expressions, illumination, and occlusions. (Formats: RAW (RGB 24-bit))
Purdue Robot Vision Lab
The MIT-CSAIL Database of Objects and Scenes- Database for testing multiclass object detection and scene recognition algorithms. Over 72,000 images with 2873 annotated frames. More than 50 annotated object classes. (Formats: jpg)
The RVL SPEC-DB (SPECularity DataBase)- A collection of over 300 real images of 100 objects taken under three different illuminaiton conditions (Diffuse/Ambient/Directed). -- Use these images to test algorithms for detecting and compensating specular highlights in color images. (Formats: TIFF )
Robot Vision Laboratory
The Xm2vts database- The XM2VTSDB contains four digital recordings of 295 people taken over a period of four months. This database contains both image and video data of faces.
Centre for Vision, Speech and Signal Processing
Traffic Image Sequences and 'Marbled Block' Sequence- thousands of frames of digitized traffic image sequences as well as the 'Marbled Block' sequence (grayscale images) (Formats: GIF)
IAKS/KOGS
U Bern Face images - hundreds of images (Formats: Sun rasterfile)
U Michigan textures (Formats: compressed raw)
U Oulu wood and knots database- Includes classifications - 1000+ color images (Formats: ppm)
UCID - an Uncompressed Colour Image Database- a benchmark database for image retrieval with predefined ground truth. (Formats: tiff)
UMass Vision Image Archive- Large image database with aerial, space, stereo, medical images and more. (Formats: homebrew)
UNC's 3D image database - many images (Formats: GIF)
USF Range Image Data with Segmentation Ground Truth- 80 image sets (Formats: Sun rasterimage)
University of Oulu Physics-based Face Database- contains color images of faces under different illuminants and camera calibration conditions as well as skin spectral reflectance measurements of each person.
Machine Vision and Media Processing Unit
University of Oulu Texture Database- Database of 320 surface textures, each captured under three illuminants, six spatial resolutions and nine rotation angles. A set of test suites is also provided so that texture segmentation, classification, and retrieval algorithms can be tested in a standard manner. (Formats: bmp, ras, xv)
Machine Vision Group
Usenix face database - Thousands of face images from many different sites (circa 994)
View Sphere Database- Images of 8 objects seen from many different view points. The view sphere is sampled using a geodesic with 172 images/sphere. Two sets for training and testing are available. (Formats: ppm)
PRIMA, GRAVIR
Vision-list Imagery Archive - Many images, many formats
Wiry Object Recognition Database- Thousands of images of a cart, ladder, stool, bicycle, chairs, and cluttered scenes with ground truth labelings of edges and regions. (Formats: jpg)
3D Vision Group
Yale Face Database- 165 images (15 individuals) with different lighting, expression, and occlusion configurations.
Yale Face Database B- 5760 single light source images of 10 subjects each seen under 576 viewing conditions (9 poses x 64 illumination conditions). (Formats: PGM)
Center for Computational Vision and Control
DeepMind QA Corpus- Textual QA corpus from CNN and DailyMail. More than 300K documents in total.Paperfor reference.
YouTube-8M Dataset- YouTube-8M is a large-scale labeled video dataset that consists of 8 million YouTube video IDs and associated labels from a diverse vocabulary of 4800 visual entities.
Open Images dataset- Open Images is a dataset of ~9 million URLs to images that have been annotated with labels spanning over 6000 categories.
Frameworks
Caffe
Torch7
Theano
cuda-convnet
convetjs
Ccv
NuPIC
DeepLearning4J
Brain
DeepLearnToolbox
Deepnet
Deeppy
JavaNN
hebel
Mocha.jl
OpenDL
cuDNN
MGL
Knet.jl
Nvidia DIGITS - a web app based on Caffe
Neon - Python based Deep Learning Framework
Keras - Theano based Deep Learning Library
Chainer - A flexible framework of neural networks for deep learning
RNNLM Toolkit
RNNLIB - A recurrent neural network library
char-rnn
MatConvNet: CNNs for MATLAB
Minerva - a fast and flexible tool for deep learning on multi-GPU
Brainstorm - Fast, flexible and fun neural networks.
Tensorflow - Open source software library for numerical computation using data flow graphs
DMTK - Microsoft Distributed Machine Learning Tookit
Scikit Flow - Simplified interface for TensorFlow (mimicking Scikit Learn)
MXnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning framework
Veles - Samsung Distributed machine learning platform
Marvin - A Minimalist GPU-only N-Dimensional ConvNets Framework
Apache SINGA - A General Distributed Deep Learning Platform
DSSTNE - Amazon's library for building Deep Learning models
SyntaxNet - Google's syntactic parser - A TensorFlow dependency library
mlpack - A scalable Machine Learning library
Torchnet - Torch based Deep Learning Library
Paddle - PArallel Distributed Deep LEarning by Baidu
NeuPy - Theano based Python library for ANN and Deep Learning
Lasagne - a lightweight library to build and train neural networks in Theano
nolearn - wrappers and abstractions around existing neural network libraries, most notably Lasagne
Sonnet - a library for constructing neural networks by Google's DeepMind
PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
CNTK - Microsoft Cognitive Toolkit
Miscellaneous
Google Plus - Deep Learning Community
Caffe Webinar
100 Best Github Resources in Github for DL
Word2Vec
Caffe DockerFile
TorontoDeepLEarning convnet
gfx.js
Torch7 Cheat sheet
Misc from MIT's 'Advanced Natural Language Processing' course
Misc from MIT's 'Machine Learning' course
Misc from MIT's 'Networks for Learning: Regression and Classification' course
Misc from MIT's 'Neural Coding and Perception of Sound' course
Implementing a Distributed Deep Learning Network over Spark
A chess AI that learns to play chess using deep learning.
Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMind
Wiki2Vec. Getting Word2vec vectors for entities and word from Wikipedia Dumps
The original code from the DeepMind article + tweaks
Google deepdream - Neural Network art
An efficient, batched LSTM.
A recurrent neural network designed to generate classical music.
Memory Networks Implementations - Facebook
Face recognition with Google's FaceNet deep neural network.
Basic digit recognition neural network
Emotion Recognition API Demo - Microsoft
Proof of concept for loading Caffe models in TensorFlow
YOLO: Real-Time Object Detection
AlphaGo - A replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search"
Machine Learning for Software Engineers
Machine Learning is Fun!
Siraj Raval's Deep Learning tutorials
Dockerface- Easy to install and use deep learning Faster R-CNN face detection for images and video in a docker container.
Awesome Deep Learning Music- Curated list of articles related to deep learning scientific research applied to music
作者：古柳_Deserts_X
链接：https://www.jianshu.com/p/e93bde4fb94d
來源：简书
简书著作权归作者所有，任何形式的转载都请联系作者获得授权并注明出处。
