本文整理了与深度学习、人工智能相关丰富的内容，涉及人工智能相关的思维导图 (+100张AI思维导图)，深度学习相关的免费在线书籍、课程、视频和讲座、论文、教程、研究人员、网站、数据集、会议、框架、工具等资源。

    内容整理自网络，源地址：https://github.com/Niraj-Lunavat/Artificial-Intelligence

    

    带链接版资源下载地址：

    链接: https://pan.baidu.com/s/1ZdA7DCtVESFvyzxMXM2o5w 

    提取码: 5cy1

 

思维导图

    大约100多张思维导图，涉及以下多方面内容。

    •Artificial Intelligence

    •Big Data

    •Data science

    •Machine Learning

    •Deep learning

    •Python Language

    •R language

    •Mathes for AI

    •Matlab

    •Neural Network

    •SQL and many more

 

深度学习优质内容

目录

    •免费书籍

    •在线视频课程

    •视频及相关讲座

    •学术论文

    •经典入门资源

    •知名研究人员

    •重要网址

    •数据集

    •重要会议

    •重要框架

    •开源工具

    •其他内容

 

在线免费书籍

    1.Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville (05/07/2015)

    2.Neural Networks and Deep Learning by Michael Nielsen (Dec 2014)

    3.Deep Learning by Microsoft Research (2013)

    4.Deep Learning Tutorial by LISA lab, University of Montreal (Jan 6 2015)

    5.neuraltalk by Andrej Karpathy : numpy-based RNN/LSTM implementation

    6.An introduction to genetic algorithms

    7.Artificial Intelligence: A Modern Approach

    8.Deep Learning in Neural Networks: An Overview

    9.Artificial intelligence and machine learning: Topic wise explanation

 

在线视频课程

    1.Machine Learning - Stanford by Andrew Ng in Coursera (2010-2014)

    2.Machine Learning - Caltech by Yaser Abu-Mostafa (2012-2014)

    3.Machine Learning - Carnegie Mellon by Tom Mitchell (Spring 2011)

    4.Neural Networks for Machine Learning by Geoffrey Hinton in Coursera (2012)

    5.Neural networks class by Hugo Larochelle from Université de Sherbrooke (2013)

    6.Deep Learning Course by CILVR lab @ NYU (2014)

    7.A.I - Berkeley by Dan Klein and Pieter Abbeel (2013)

    8.A.I - MIT by Patrick Henry Winston (2010)

    9.Vision and learning - computers and brains by Shimon Ullman, Tomaso Poggio, Ethan Meyers @ MIT (2013)

    10.Convolutional Neural Networks for Visual Recognition - Stanford by Fei-Fei Li, Andrej Karpathy (2017)

    11.Deep Learning for Natural Language Processing - Stanford

    12.Neural Networks - usherbrooke

    13.Machine Learning - Oxford (2014-2015)

    14.Deep Learning - Nvidia (2015)

    15.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)

    16.Deep Learning - Udacity/Google by Vincent Vanhoucke and Arpan Chakraborty (2016)

    17.Deep Learning - UWaterloo by Prof. Ali Ghodsi at University of Waterloo (2015)

    18.Statistical Machine Learning - CMU by Prof. Larry Wasserman

    19.Deep Learning Course by Yann LeCun (2016)

    20.Designing, Visualizing and Understanding Deep Neural Networks-UC Berkeley

    21.UVA Deep Learning Course MSc in Artificial Intelligence for the University of Amsterdam.

    22.MIT 6.S094: Deep Learning for Self-Driving Cars

    23.MIT 6.S191: Introduction to Deep Learning

    24.Berkeley CS 294: Deep Reinforcement Learning

    25.Keras in Motion video course

    26.Practical Deep Learning For Coders by Jeremy Howard - Fast.ai

    27.Introduction to Deep Learning by Prof. Bhiksha Raj (2017)

    28.AI for Everyone by Andrew Ng (2019)

 

视频及课程

    1.How To Create A Mind By Ray Kurzweil

    2.Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng

    3.Recent Developments in Deep Learning By Geoff Hinton

    4.The Unreasonable Effectiveness of Deep Learning by Yann LeCun

    5.Deep Learning of Representations by Yoshua bengio

    6.Principles of Hierarchical Temporal Memory by Jeff Hawkins

    7.Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab by Adam Coates

    8.Making Sense of the World with Deep Learning By Adam Coates

    9.Demystifying Unsupervised Feature Learning By Adam Coates

    10.Visual Perception with Deep Learning By Yann LeCun

    11.The Next Generation of Neural Networks By Geoffrey Hinton at GoogleTechTalks

    12.The wonderful and terrifying implications of computers that can learn By Jeremy Howard at TEDxBrussels

    13.Unsupervised Deep Learning - Stanford by Andrew Ng in Stanford (2011)

    14.Natural Language Processing By Chris Manning in Stanford

    15.A beginners Guide to Deep Neural Networks By Natalie Hammel and Lorraine Yurshansky

    16.Deep Learning: Intelligence from Big Data by Steve Jurvetson (and panel) at VLAB in Stanford.

    17.Introduction to Artificial Neural Networks and Deep Learning by Leo Isikdogan at Motorola Mobility HQ

    18.NIPS 2016 lecture and workshop videos - NIPS 2016

    19.Deep Learning Crash Course: a series of mini-lectures by Leo Isikdogan on YouTube (2018)

 

经典论文

    You can also find the most cited deep learning papers from here

    1.ImageNet Classification with Deep Convolutional Neural Networks

    2.Using Very Deep Autoencoders for Content Based Image Retrieval

    3.Learning Deep Architectures for AI

    4.CMU’s list of papers

    5.Neural Networks for Named Entity Recognition zip

    6.Training tricks by YB

    7.Geoff Hinton's reading list (all papers)

    8.Supervised Sequence Labelling with Recurrent Neural Networks

    9.Statistical Language Models based on Neural Networks

    10.Training Recurrent Neural Networks

    11.Recursive Deep Learning for Natural Language Processing and Computer Vision

    12.Bi-directional RNN

    13.LSTM

    14.GRU - Gated Recurrent Unit

    15.GFRNN . .

    16.LSTM: A Search Space Odyssey

    17.A Critical Review of Recurrent Neural Networks for Sequence Learning

    18.Visualizing and Understanding Recurrent Networks

    19.Wojciech Zaremba, Ilya Sutskever, An Empirical Exploration of Recurrent Network Architectures

    20.Recurrent Neural Network based Language Model

    21.Extensions of Recurrent Neural Network Language Model

    22.Recurrent Neural Network based Language Modeling in Meeting Recognition

    23.Deep Neural Networks for Acoustic Modeling in Speech Recognition

    24.Speech Recognition with Deep Recurrent Neural Networks

    25.Reinforcement Learning Neural Turing Machines

    26.Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

    27.Google - Sequence to Sequence Learning with Neural Networks

    28.Memory Networks

    29.Policy Learning with Continuous Memory States for Partially Observed Robotic Control

    30.Microsoft - Jointly Modeling Embedding and Translation to Bridge Video and Language

    31.Neural Turing Machines

    32.Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

    33.Mastering the Game of Go with Deep Neural Networks and Tree Search

    34.Batch Normalization

    35.Residual Learning

    36.Image-to-Image Translation with Conditional Adversarial Networks

    37.Berkeley AI Research (BAIR) Laboratory

    38.MobileNets by Google

    39.Cross Audio-Visual Recognition in the Wild Using Deep Learning

    40.Dynamic Routing Between Capsules

    41.Matrix Capsules With Em Routing

    42.Efficient BackProp

 

指导教程汇总

    1.UFLDL Tutorial 1

    2.UFLDL Tutorial 2

    3.Deep Learning for NLP (without Magic)

    4.A Deep Learning Tutorial: From Perceptrons to Deep Networks

    5.Deep Learning from the Bottom up

    6.Theano Tutorial

    7.Neural Networks for Matlab

    8.Using convolutional neural nets to detect facial keypoints tutorial

    9.Torch7 Tutorials

    10.The Best Machine Learning Tutorials On The Web

    11.VGG Convolutional Neural Networks Practical

    12.TensorFlow tutorials

    13.More TensorFlow tutorials

    14.TensorFlow Python Notebooks

    15.Keras and Lasagne Deep Learning Tutorials

    16.Classification on raw time series in TensorFlow with a LSTM RNN

    17.Using convolutional neural nets to detect facial keypoints tutorial

    18.TensorFlow-World

    19.Deep Learning with Python

    20.Grokking Deep Learning

    21.Deep Learning for Search

    22.Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder

    23.Pytorch Tutorial by Yunjey Choi

 

知名学者

    1.Aaron Courville

    2.Abdel-rahman Mohamed

    3.Adam Coates

    4.Alex Acero

    5.Alex Krizhevsky

    6.Alexander Ilin

    7.Amos Storkey

    8.Andrej Karpathy

    9.Andrew M. Saxe

    10.Andrew Ng

    11.Andrew W. Senior

    12.Andriy Mnih

    13.Ayse Naz Erkan

    14.Benjamin Schrauwen

    15.Bernardete Ribeiro

    16.Bo David Chen

    17.Boureau Y-Lan

    18.Brian Kingsbury

    19.Christopher Manning

    20.Clement Farabet

    21.Dan Claudiu Cireșan

    22.David Reichert

    23.Derek Rose

    24.Dong Yu

    25.Drausin Wulsin

    26.Erik M. Schmidt

    27.Eugenio Culurciello

    28.Frank Seide

    29.Galen Andrew

    30.Geoffrey Hinton

    31.George Dahl

    32.Graham Taylor

    33.Grégoire Montavon

    34.Guido Francisco Montúfar

    35.Guillaume Desjardins

    36.Hannes Schulz

    37.Hélène Paugam-Moisy

    38.Honglak Lee

    39.Hugo Larochelle

    40.Ilya Sutskever

    41.Itamar Arel

    42.James Martens

    43.Jason Morton

    44.Jason Weston

    45.Jeff Dean

    46.Jiquan Mgiam

    47.Joseph Turian

    48.Joshua Matthew Susskind

    49.Jürgen Schmidhuber

    50.Justin A. Blanco

    51.Koray Kavukcuoglu

    52.KyungHyun Cho

    53.Li Deng

    54.Lucas Theis

    55.Ludovic Arnold

    56.Marc'Aurelio Ranzato

    57.Martin Längkvist

    58.Misha Denil

    59.Mohammad Norouzi

    60.Nando de Freitas

    61.Navdeep Jaitly

    62.Nicolas Le Roux

    63.Nitish Srivastava

    64.Noel Lopes

    65.Oriol Vinyals

    66.Pascal Vincent

    67.Patrick Nguyen

    68.Pedro Domingos

    69.Peggy Series

    70.Pierre Sermanet

    71.Piotr Mirowski

    72.Quoc V. Le

    73.Reinhold Scherer

    74.Richard Socher

    75.Rob Fergus

    76.Robert Coop

    77.Robert Gens

    78.Roger Grosse

    79.Ronan Collobert

    80.Ruslan Salakhutdinov

    81.Sebastian Gerwinn

    82.Stéphane Mallat

    83.Sven Behnke

    84.Tapani Raiko

    85.Tara Sainath

    86.Tijmen Tieleman

    87.Tom Karnowski

    88.Tomáš Mikolov

    89.Ueli Meier

    90.Vincent Vanhoucke

    91.Volodymyr Mnih

    92.Yann LeCun

    93.Yichuan Tang

    94.Yoshua Bengio

    95.Yotaro Kubo

    96.Youzhi (Will) Zou

    97.Fei-Fei Li

    98.Ian Goodfellow

    99.Robert Laganière

 

重要网站

    1.deeplearning.net

    2.deeplearning.stanford.edu

    3.nlp.stanford.edu

    4.ai-junkie.com

    5.cs.brown.edu/research/ai

    6.eecs.umich.edu/ai

    7.cs.utexas.edu/users/ai-lab

    8.cs.washington.edu/research/ai

    9.aiai.ed.ac.uk

    10.www-aig.jpl.nasa.gov

    11.csail.mit.edu

    12.cgi.cse.unsw.edu.au/~aishare

    13.cs.rochester.edu/research/ai

    14.ai.sri.com

    15.isi.edu/AI/isd.htm

    16.nrl.navy.mil/itd/aic

    17.hips.seas.harvard.edu

    18.AI Weekly

    19.stat.ucla.edu

    20.deeplearning.cs.toronto.edu

    21.jeffdonahue.com/lrcn/

    22.visualqa.org

    23.www.mpi-inf.mpg.de/departments/computer-vision...

    24.Deep Learning News

    25.Machine Learning is Fun! Adam Geitgey's Blog

    26.Guide to Machine Learning

    27.Deep Learning for Beginners

 

公开数据集

    1.MNIST Handwritten digits

    2.Google House Numbers from street view

    3.CIFAR-10 and CIFAR-100

    4.IMAGENET

    5.Tiny Images 80 Million tiny images6.

    6.Flickr Data 100 Million Yahoo dataset

    7.Berkeley Segmentation Dataset 500

    8.UC Irvine Machine Learning Repository

    9.Flickr 8k

    10.Flickr 30k

    11.Microsoft COCO

    12.VQA

    13.Image QA

    14.AT&T Laboratories Cambridge face database

    15.AVHRR Pathfinder

    16.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)

    17.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)

    18.Annotated face, hand, cardiac & meat images - Most images & annotations are supplemented by various ASM/AAM analyses using the AAM-API. (Formats: bmp,asf)

    19.Image Analysis and Computer Graphics

    20.Brown University Stimuli - A variety of datasets including geons, objects, and "greebles". Good for testing recognition algorithms. (Formats: pict)

    21.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)

    22.Machine Vision Unit

    23.CCITT Fax standard images - 8 images (Formats: gif)

    24.CMU CIL's Stereo Data with Ground Truth - 3 sets of 11 images, including color tiff images with spectroradiometry (Formats: gif, tiff)

    25.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.

    26.CMU VASC Image Database - Images, sequences, stereo pairs (thousands of images) (Formats: Sun Rasterimage)

    27.Caltech Image Database - about 20 images - mostly top-down views of small objects and toys. (Formats: GIF)

    28.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)

    29.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)

    30.Computational Vision Lab

    31.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)

    32.Efficient Content-based Retrieval Group

    33.Densely Sampled View Spheres - Densely sampled view spheres - upper half of the view sphere of two toy objects with 2500 images each. (Formats: tiff)

    34.Computer Science VII (Graphical Systems)

    35.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)

    36.Univerity of Minnesota Vision Lab

    37.El Salvador Atlas of Gastrointestinal VideoEndoscopy - Images and Videos of his-res of studies taken from Gastrointestinal Video endoscopy. (Formats: jpg, mpg, gif)

    38.FG-NET Facial Aging Database - Database contains 1002 face images showing subjects at different ages. (Formats: jpg)

    39.FVC2000 Fingerprint Databases - FVC2000 is the First International Competition for Fingerprint Verification Algorithms. Four fingerprint databases constitute the FVC2000 benchmark (3520 fingerprints in all).

    40.Biometric Systems Lab - University of Bologna

    41.Face and Gesture images and image sequences - Several image datasets of faces and gestures that are ground truth annotated for benchmarking

    42.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)

    43.Language Processing and Pattern Recognition

    44.Groningen Natural Image Database - 4000+ 1536x1024 (16 bit) calibrated outdoor images (Formats: homebrew)

    45.ICG Testhouse sequence - 2 turntable sequences from ifferent viewing heights, 36 images each, resolution 1000x750, color (Formats: PPM)

    46.Institute of Computer Graphics and Vision

    47.IEN Image Library - 1000+ images, mostly outdoor sequences (Formats: raw, ppm)

    48.INRIA's Syntim images database - 15 color image of simple objects (Formats: gif)

    49.INRIA

    50.INRIA's Syntim stereo databases - 34 calibrated color stereo pairs (Formats: gif)

    51.Image Analysis Laboratory - Images obtained from a variety of imaging modalities -- raw CFA images, range images and a host of "medical images". (Formats: homebrew)

    52.Image Analysis Laboratory

    53.Image Database - An image database including some textures

    54.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.)

    55.ATR Research, Kyoto, Japan

    56.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)

    57.MIT Vision Texture - Image archive (100+ images) (Formats: ppm)

    58.MIT face images and more - hundreds of images (Formats: homebrew)

    59.Machine Vision - Images from the textbook by Jain, Kasturi, Schunck (20+ images) (Formats: GIF TIFF)

    60.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)

    61.ftp://ftp.cps.msu.edu/pub/prip - many images (Formats: unknown)

    62.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)

    63.Middlebury Stereo Vision Research Page - Middlebury College

    64.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)

    65.NIST Fingerprint and handwriting - datasets - thousands of images (Formats: unknown)

    66.NIST Fingerprint data - compressed multipart uuencoded tar file

    67.NLM HyperDoc Visible Human Project - Color, CAT and MRI image samples - over 30 images (Formats: jpeg)

    68.National Design Repository - Over 55,000 3D CAD and solid models of (mostly) mechanical/machined engineerign designs. (Formats: gif,vrml,wrl,stp,sat)

    69.Geometric & Intelligent Computing Laboratory

    70.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)

    71.OSU (MSU/WSU) Range Image Database - Hundreds of real and synthetic images (Formats: gif, homebrew)

    72.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)

    73.Signal Analysis and Machine Perception Laboratory

    74.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)

    75.Vision Research Group

    76.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))

    77.LIMSI-CNRS/CHM/IMM/vision

    78.LIMSI-CNRS

    79.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)

    80.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)

    81.Computer Vision Group

    82.Sequences for Flow Based Reconstruction - synthetic sequence for testing structure from motion algorithms (Formats: pgm)

    83.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)

    84.Stuttgart Range Image Database - A collection of synthetic range images taken from high-resolution polygonal models available on the web (Formats: homebrew)

    85.Department Image Understanding

    86.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))

    87.Purdue Robot Vision Lab

    88.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)

    89.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 )

    90.Robot Vision Laboratory

    91.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.

    92.Centre for Vision, Speech and Signal Processing

    93.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)

    94.IAKS/KOGS

    95.U Bern Face images - hundreds of images (Formats: Sun rasterfile)

    96.U Michigan textures (Formats: compressed raw)

    97.U Oulu wood and knots database - Includes classifications - 1000+ color images (Formats: ppm)

    98.UCID - an Uncompressed Colour Image Database - a benchmark database for image retrieval with predefined ground truth. (Formats: tiff)

    99.UMass Vision Image Archive - Large image database with aerial, space, stereo, medical images and more. (Formats: homebrew)

    100.UNC's 3D image database - many images (Formats: GIF)

    101.USF Range Image Data with Segmentation Ground Truth - 80 image sets (Formats: Sun rasterimage)

    102.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.

    103.Machine Vision and Media Processing Unit

    104.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)

    105.Machine Vision Group

    106.Usenix face database - Thousands of face images from many different sites (circa 994)

    107.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)

    108.PRIMA, GRAVIR

    109.Vision-list Imagery Archive - Many images, many formats

    110.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)

    111.3D Vision Group

    112.Yale Face Database - 165 images (15 individuals) with different lighting, expression, and occlusion configurations.

    113.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)

    114.Center for Computational Vision and Control

    115.DeepMind QA Corpus - Textual QA corpus from CNN and DailyMail. More than 300K documents in total. Paper for reference.

    116.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.

    117.Open Images dataset - Open Images is a dataset of ~9 million URLs to images that have been annotated with labels spanning over 6000 categories.

    118.Visual Object Classes Challenge 2012 (VOC2012) - VOC2012 dataset containing 12k images with 20 annotated classes for object detection and segmentation.

    119.Fashion-MNIST - MNIST like fashion product dataset consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.

    120.Large-scale Fashion (DeepFashion) Database - Contains over 800,000 diverse fashion images. Each image in this dataset is labeled with 50 categories, 1,000 descriptive attributes, bounding box and clothing landmarks

    121.FakeNewsCorpus - Contains about 10 million news articles classified using opensources.co types

 

重要会议

    1.CVPR - IEEE Conference on Computer Vision and Pattern Recognition

    2.AAMAS - International Joint Conference on Autonomous Agents and Multiagent Systems

    3.IJCAI - International Joint Conference on Artificial Intelligence

    4.ICML - International Conference on Machine Learning

    5.ECML - European Conference on Machine Learning

    6.KDD - Knowledge Discovery and Data Mining

    7.NIPS - Neural Information Processing Systems

    8.O'Reilly AI Conference - O'Reilly Artificial Intelligence Conference

    9.ICDM - International Conference on Data Mining

    10.ICCV - International Conference on Computer Vision

    11.AAAI - Association for the Advancement of Artificial Intelligence

 

经典架构

    1.Caffe

    2.Torch7

    3.Theano

    4.cuda-convnet

    5.convetjs

    6.Ccv

    7.NuPIC

    8.DeepLearning4J

    9.Brain

    10.DeepLearnToolbox

    11.Deepnet

    12.Deeppy

    13.JavaNN

    14.hebel

    15.Mocha.jl

    16.OpenDL

    17.cuDNN

    18.MGL

    19.Knet.jl

    20.Nvidia DIGITS - a web app based on Caffe

    21.Neon - Python based Deep Learning Framework

    22.Keras - Theano based Deep Learning Library

    23.Chainer - A flexible framework of neural networks for deep learning

    24.RNNLM Toolkit

    25.RNNLIB - A recurrent neural network library

    26.char-rnn

    27.MatConvNet: CNNs for MATLAB

    28.Minerva - a fast and flexible tool for deep learning on multi-GPU

    29.Brainstorm - Fast, flexible and fun neural networks.

    30.Tensorflow - Open source software library for numerical computation using data flow graphs

    31.DMTK - Microsoft Distributed Machine Learning Tookit

    32.Scikit Flow - Simplified interface for TensorFlow (mimicking Scikit Learn)

    33.MXnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning framework

    34.Veles - Samsung Distributed machine learning platform

    35.Marvin - A Minimalist GPU-only N-Dimensional ConvNets Framework

    36.Apache SINGA - A General Distributed Deep Learning Platform

    37.DSSTNE - Amazon's library for building Deep Learning models

    38.SyntaxNet - Google's syntactic parser - A TensorFlow dependency library

    39.mlpack - A scalable Machine Learning library

    40.Torchnet - Torch based Deep Learning Library

    41.Paddle - PArallel Distributed Deep LEarning by Baidu

    42.NeuPy - Theano based Python library for ANN and Deep Learning

    43.Lasagne - a lightweight library to build and train neural networks in Theano

    44.nolearn - wrappers and abstractions around existing neural network libraries, most notably Lasagne

    45.Sonnet - a library for constructing neural networks by Google's DeepMind

    46.PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

    47.CNTK - Microsoft Cognitive Toolkit

    48.Serpent.AI - Game agent framework: Use any video game as a deep learning sandbox

    49.Caffe2 - A New Lightweight, Modular, and Scalable Deep Learning Framework

    50.deeplearn.js - Hardware-accelerated deep learning and linear algebra (NumPy) library for the web

    51.TensorForce - A TensorFlow library for applied reinforcement learning

    52.Coach - Reinforcement Learning Coach by Intel® AI Lab

    53.albumentations - A fast and framework agnostic image augmentation library

 

工具集合

    1.Netron - Visualizer for deep learning and machine learning models

    2.Jupyter Notebook - Web-based notebook environment for interactive computing

    3.TensorBoard - TensorFlow's Visualization Toolkit

    4.Visual Studio Tools for AI - Develop, debug and deploy deep learning and AI solutions

 

其他内容

    1.Google Plus - Deep Learning Community

    2.Caffe Webinar

    3.100 Best Github Resources in Github for DL

    4.Word2Vec

    5.Caffe DockerFile

    6.TorontoDeepLEarning convnet

    7.gfx.js

    8.Torch7 Cheat sheet

    9.Misc from MIT's 'Advanced Natural Language Processing' course

    10.Misc from MIT's 'Machine Learning' course

    11.Misc from MIT's 'Networks for Learning: Regression and Classification' course

    12.Misc from MIT's 'Neural Coding and Perception of Sound' course

    13.Implementing a Distributed Deep Learning Network over Spark

    14.A chess AI that learns to play chess using deep learning.

    15.Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMind

    16.Wiki2Vec. Getting Word2vec vectors for entities and word from Wikipedia Dumps

    17.The original code from the DeepMind article + tweaks

    18.Google deepdream - Neural Network art

    19.An efficient, batched LSTM.

    20.A recurrent neural network designed to generate classical music.

    21.Memory Networks Implementations - Facebook

    22.Face recognition with Google's FaceNet deep neural network.

    23.Basic digit recognition neural network

    24.Emotion Recognition API Demo - Microsoft

    25.Proof of concept for loading Caffe models in TensorFlow

    26.YOLO: Real-Time Object Detection

    27.AlphaGo - A replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search"

    28.Machine Learning for Software Engineers

    29.Machine Learning is Fun!

    30.Siraj Raval's Deep Learning tutorials

    31.Dockerface - Easy to install and use deep learning Faster R-CNN face detection for images and video in a docker container.

    32.Awesome Deep Learning Music - Curated list of articles related to deep learning scientific research applied to music

    33.Awesome Graph Embedding - Curated list of articles related to deep learning scientific research on graph structured data