Awesome Deep Learning

A curated list of awesome Deep Learning tutorials, projects and communities.
By ChristosChristofidis

deep-learning machine-learning neural-network awesome-list awesome deep-learning-tutorial recurrent-networks deep-networks face-images

# Awesome Deep Learning

Table of Contents


  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
    10.Grokking Deep Learning for Computer Vision

  10. Dive into Deep Learning - numpy based interactive Deep Learning book

  11. Practical Deep Learning for Cloud, Mobile, and Edge - A book for optimization techniques during production.

  12. Math and Architectures of Deep Learning - by Krishnendu Chaudhury

  13. TensorFlow 2.0 in Action - by Thushan Ganegedara


  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 -

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

  28. AI for Everyone by Andrew Ng (2019)

  29. MIT Intro to Deep Learning 7 day bootcamp - A seven day bootcamp designed in MIT to introduce deep learning methods and applications (2019)

  30. Deep Blueberry: Deep Learning - A free five-weekend plan to self-learners to learn the basics of deep-learning architectures like CNNs, LSTMs, RNNs, VAEs, GANs, DQN, A3C and more (2019)

  31. Spinning Up in Deep Reinforcement Learning - A free deep reinforcement learning course by OpenAI (2019)

  32. Deep Learning Specialization - Coursera - Breaking into AI with the best course from Andrew NG.

  33. Deep Learning - UC Berkeley | STAT-157 by Alex Smola and Mu Li (2019)

  34. Machine Learning for Mere Mortals video course by Nick Chase

  35. Machine Learning Crash Course with TensorFlow APIs -Google AI

  36. Deep Learning from the Foundations Jeremy Howard -

  37. Deep Reinforcement Learning (nanodegree) - Udacity a 3-6 month Udacity nanodegree, spanning multiple courses (2018)

  38. Grokking Deep Learning in Motion by Beau Carnes (2018)

  39. Face Detection with Computer Vision and Deep Learning by Hakan Cebeci

  40. Deep Learning Online Course list at Classpert List of Deep Learning online courses (some are free) from Classpert Online Course Search

  41. AWS Machine Learning Machine Learning and Deep Learning Courses from Amazon's Machine Learning unviersity

  42. Intro to Deep Learning with PyTorch - A great introductory course on Deep Learning by Udacity and Facebook AI

  43. Deep Learning by Kaggle - Kaggle's free course on Deep Learning

Videos and Lectures

  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)

  20. Deep Learning Crash Course By Oliver Zeigermann

  21. Deep Learning with R in Motion: a live video course that teaches how to apply deep learning to text and images using the powerful Keras library and its R language interface.

  22. Medical Imaging with Deep Learning Tutorial: This tutorial is styled as a graduate lecture about medical imaging with deep learning. This will cover the background of popular medical image domains (chest X-ray and histology) as well as methods to tackle multi-modality/view, segmentation, and counting tasks.

  23. Deepmind x UCL Deeplearning: 2020 version

  24. Deepmind x UCL Reinforcement Learning: Deep Reinforcement Learning

  25. CMU 11-785 Intro to Deep learning Spring 2020 Course: 11-785, Intro to Deep Learning by Bhiksha Raj

  26. Machine Learning CS 229 : End part focuses on deep learning By Andrew Ng


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

  43. Generative Adversarial Nets

  44. Fast R-CNN

  45. FaceNet: A Unified Embedding for Face Recognition and Clustering

  46. Siamese Neural Networks for One-shot Image Recognition

  47. Unsupervised Translation of Programming Languages

  48. Matching Networks for One Shot Learning


  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

  24. Understanding deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras

  25. Overview and benchmark of traditional and deep learning models in text classification

  26. Hardware for AI: Understanding computer hardware & build your own computer

  27. Programming Community Curated Resources

  28. The Illustrated Self-Supervised Learning

  29. Visual Paper Summary: ALBERT (A Lite BERT)

  30. Semi-Supervised Deep Learning with GANs for Melanoma Detection

  31. Named Entity Recognition using Reformers

  32. Deep N-Gram Models on Shakespeare’s works

  33. Wide Residual Networks

  34. Fashion MNIST using Flax

  35. Fake News Classification (with streamlit deployment)

  36. Regression Analysis for Primary Biliary Cirrhosis

  37. Cross Matching Methods for Astronomical Catalogs

  38. Named Entity Recognition using BiDirectional LSTMs

  39. Image Recognition App using Tflite and Flutter


  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

  100. Merve Ayyüce Kızrak



















  18. AI Weekly






  24. Deep Learning News

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

  26. Guide to Machine Learning

  27. Deep Learning for Beginners

  28. Machine Learning Mastery blog

  29. ML Compiled

  30. Programming Community Curated Resources

  31. A Beginner's Guide To Understanding Convolutional Neural Networks



  34. AI Summer

  35. AI Hub - supported by AAAI, NeurIPS

  36. CatalyzeX: Machine Learning Hub for Builders and Makers

  37. The Epic Code


  1. MNIST Handwritten digits

  2. Google House Numbers from street view

  3. CIFAR-10 and CIFAR-100


  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. - 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 engineering 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. - 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


  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)


  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)


  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 types


  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

  12. MAIS - Montreal AI Symposium


  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. TVM - End to End Deep Learning Compiler Stack for CPUs, GPUs and specialized accelerators

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

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

  54. Neuraxle - A general-purpose ML pipelining framework

  55. Catalyst: High-level utils for PyTorch DL & RL research. It was developed with a focus on reproducibility, fast experimentation and code/ideas reusing

  56. garage - A toolkit for reproducible reinforcement learning research

  57. Detecto - Train and run object detection models with 5-10 lines of code

  58. Karate Club - An unsupervised machine learning library for graph structured data

  59. Synapses - A lightweight library for neural networks that runs anywhere

  60. TensorForce - A TensorFlow library for applied reinforcement learning

  61. Hopsworks - A Feature Store for ML and Data-Intensive AI

  62. Feast - A Feature Store for ML for GCP by Gojek/Google

  63. PyTorch Geometric Temporal - Representation learning on dynamic graphs

  64. lightly - A computer vision framework for self-supervised learning

  65. Trax — Deep Learning with Clear Code and Speed

  66. Flax - a neural network ecosystem for JAX that is designed for flexibility

  67. QuickVision


  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

  5. TensorWatch - Debugging and visualization for deep learning

  6. ML Workspace - All-in-one web-based IDE for machine learning and data science.

  7. dowel - A little logger for machine learning research. Log any object to the console, CSVs, TensorBoard, text log files, and more with just one call to logger.log()

  8. Neptune - Lightweight tool for experiment tracking and results visualization.

  9. CatalyzeX - Browser extension (Chrome and Firefox) that automatically finds and links to code implementations for ML papers anywhere online: Google, Twitter, Arxiv, Scholar, etc.

  10. Determined - Deep learning training platform with integrated support for distributed training, hyperparameter tuning, smart GPU scheduling, experiment tracking, and a model registry.

  11. DAGsHub - Community platform for Open Source ML – Manage experiments, data & models and create collaborative ML projects easily.


  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. YOLO: Practical Implementation using Python

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

  29. Machine Learning for Software Engineers

  30. Machine Learning is Fun!

  31. Siraj Raval's Deep Learning tutorials

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

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

  34. Awesome Graph Embedding - Curated list of articles related to deep learning scientific research on graph structured data at the graph level.

  35. Awesome Network Embedding - Curated list of articles related to deep learning scientific research on graph structured data at the node level.

  36. Microsoft Recommenders contains examples, utilities and best practices for building recommendation systems. Implementations of several state-of-the-art algorithms are provided for self-study and customization in your own applications.

  37. The Unreasonable Effectiveness of Recurrent Neural Networks - Andrej Karpathy blog post about using RNN for generating text.

  38. Ladder Network - Keras Implementation of Ladder Network for Semi-Supervised Learning

  39. toolbox: Curated list of ML libraries

  40. CNN Explainer

  41. AI Expert Roadmap - Roadmap to becoming an Artificial Intelligence Expert


Have anything in mind that you think is awesome and would fit in this list? Feel free to send a pull request.


To the extent possible under law, Christos Christofidis has waived all copyright and related or neighboring rights to this work.