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最全深度学习资源集合(Github:Awesome Deep Learning)

[日期:2017-11-27] 来源:简书  作者: [字体: ]
 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

Python爬虫和数据可视化


作者:Deserts_X
链接:http://www.jianshu.com/p/e93bde4fb94d
來源:简书
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