# Awesome Deep Learning

## Table of Contents

- Books

- Courses

- Videos and Lectures

- Papers

- Tutorials

- Researchers

- Websites

- Datasets

- Conferences

- Frameworks

- Tools

- Miscellaneous

- Contributing

### Books

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
11. Dive into Deep Learning - numpy based interactive Deep Learning book
12. Practical Deep Learning for Cloud, Mobile, and Edge - A book for optimization techniques during production.
13. Math and Architectures of Deep Learning - by Krishnendu Chaudhury
14. TensorFlow 2.0 in Action - by Thushan Ganegedara
15. Deep Learning for Natural Language Processing - by Stephan Raaijmakers
16. Deep Learning Patterns and Practices - by Andrew Ferlitsch
17. Inside Deep Learning - by Edward Raff
18. Deep Learning with Python, Second Edition - by François Chollet
19. Evolutionary Deep Learning - by Micheal Lanham
20. Engineering Deep Learning Platforms - by Chi Wang and Donald Szeto
21. Deep Learning with R, Second Edition - by François Chollet with Tomasz Kalinowski and J. J. Allaire
22. Regularization in Deep Learning - by Liu Peng
23. Jax in Action - by Grigory Sapunov
24. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron | Oct 15, 2019

### Courses

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)
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 - Fast.ai
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 university
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
44. Yann LeCun’s Deep Learning Course at CDS - DS-GA 1008 · SPRING 2021
45. Neural Networks and Deep Learning - COMP9444 19T3
46. Deep Learning A.I.Shelf

### 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
27. What is Neural Structured Learning by Andrew Ferlitsch
28. Deep Learning Design Patterns by Andrew Ferlitsch
29. Architecture of a Modern CNN: the design pattern approach by Andrew Ferlitsch
30. Metaparameters in a CNN by Andrew Ferlitsch
31. Multi-task CNN: a real-world example by Andrew Ferlitsch
32. A friendly introduction to deep reinforcement learning by Luis Serrano
33. What are GANs and how do they work? by Edward Raff
34. Coding a basic WGAN in PyTorch by Edward Raff
35. Training a Reinforcement Learning Agent by Miguel Morales
36. Understand what is Deep Learning

### Papers

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
49. VOLO: Vision Outlooker for Visual Recognition
50. ViT: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
51. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
52. DeepFaceDrawing: Deep Generation of Face Images from Sketches

### Tutorials

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

## Researchers

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

### Websites

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
28. Machine Learning Mastery blog
29. ML Compiled
30. Programming Community Curated Resources
31. A Beginner’s Guide To Understanding Convolutional Neural Networks
32. ahmedbesbes.com
33. amitness.com
34. AI Summer
35. AI Hub - supported by AAAI, NeurIPS
36. CatalyzeX: Machine Learning Hub for Builders and Makers
37. The Epic Code
38. all AI news

### Datasets

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 different 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 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. 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
122. LLVIP - 15488 visible-infrared paired images (30976 images) for low-light vision research, Project_Page
123. MSDA - Over over 5 million images from 5 different domains for multi-source ocr/text recognition DA research, Project_Page
124. SANAD: Single-Label Arabic News Articles Dataset for Automatic Text Categorization - SANAD Dataset is a large collection of Arabic news articles that can be used in different Arabic NLP tasks such as Text Classification and Word Embedding. The articles were collected using Python scripts written specifically for three popular news websites: AlKhaleej, AlArabiya and Akhbarona.
125. Referit3D - Two large-scale and complementary visio-linguistic datasets (aka Nr3D and Sr3D) for identifying fine-grained 3D objects in ScanNet scenes. Nr3D contains 41.5K natural, free-form utterances, and Sr3d contains 83.5K template-based utterances.
126. SQuAD - Stanford released ~100,000 English QA pairs and ~50,000 unanswerable questions
127. FQuAD - ~25,000 French QA pairs released by Illuin Technology
128. GermanQuAD and GermanDPR - deepset released ~14,000 German QA pairs
129. SberQuAD - Sberbank released ~90,000 Russian QA pairs
130. ArtEmis - Contains 450K affective annotations of emotional responses and linguistic explanations for 80,000 artworks of WikiArt.

### Conferences

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

### Frameworks

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
68. Colossal-AI - An Integrated Large-scale Model Training System with Efficient Parallelization Techniques
69. haystack: an open-source neural search framework
70. Maze - Application-oriented deep reinforcement learning framework addressing real-world decision problems.
71. InsNet - A neural network library for building instance-dependent NLP models with padding-free dynamic batching

### Tools

1. Nebullvm - Easy-to-use library to boost deep learning inference leveraging multiple deep learning compilers.
2. Netron - Visualizer for deep learning and machine learning models
3. Jupyter Notebook - Web-based notebook environment for interactive computing
4. TensorBoard - TensorFlow’s Visualization Toolkit
5. Visual Studio Tools for AI - Develop, debug and deploy deep learning and AI solutions
6. TensorWatch - Debugging and visualization for deep learning
7. ML Workspace - All-in-one web-based IDE for machine learning and data science.
8. 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()`
9. Neptune - Lightweight tool for experiment tracking and results visualization.
10. CatalyzeX - Browser extension (Chrome and Firefox) that automatically finds and links to code implementations for ML papers anywhere online: Google, Twitter, Arxiv, Scholar, etc.
11. Determined - Deep learning training platform with integrated support for distributed training, hyperparameter tuning, smart GPU scheduling, experiment tracking, and a model registry.
12. DAGsHub - Community platform for Open Source ML – Manage experiments, data & models and create collaborative ML projects easily.
13. hub - Fastest unstructured dataset management for TensorFlow/PyTorch by activeloop.ai. Stream & version-control data. Converts large data into single numpy-like array on the cloud, accessible on any machine.
14. DVC - DVC is built to make ML models shareable and reproducible. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.
15. CML - CML helps you bring your favorite DevOps tools to machine learning.
16. MLEM - MLEM is a tool to easily package, deploy and serve Machine Learning models. It seamlessly supports a variety of scenarios like real-time serving and batch processing.

### Miscellaneous

1. Caffe Webinar
2. 100 Best Github Resources in Github for DL
3. Word2Vec
4. Caffe DockerFile
5. TorontoDeepLEarning convnet
6. gfx.js
7. Torch7 Cheat sheet
8. Misc from MIT’s ‘Advanced Natural Language Processing’ course
9. Misc from MIT’s ‘Machine Learning’ course
10. Misc from MIT’s ‘Networks for Learning: Regression and Classification’ course
11. Misc from MIT’s ‘Neural Coding and Perception of Sound’ course
12. Implementing a Distributed Deep Learning Network over Spark
13. A chess AI that learns to play chess using deep learning.
14. Reproducing the results of “Playing Atari with Deep Reinforcement Learning” by DeepMind
15. Wiki2Vec. Getting Word2vec vectors for entities and word from Wikipedia Dumps
16. The original code from the DeepMind article + tweaks
17. Google deepdream - Neural Network art
18. An efficient, batched LSTM.
19. A recurrent neural network designed to generate classical music.
20. Memory Networks Implementations - Facebook
21. Face recognition with Google’s FaceNet deep neural network.
22. Basic digit recognition neural network
23. Emotion Recognition API Demo - Microsoft
24. Proof of concept for loading Caffe models in TensorFlow
25. YOLO: Real-Time Object Detection
26. YOLO: Practical Implementation using Python
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 at the graph level.
34. Awesome Network Embedding - Curated list of articles related to deep learning scientific research on graph structured data at the node level.
35. 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.
36. The Unreasonable Effectiveness of Recurrent Neural Networks - Andrej Karpathy blog post about using RNN for generating text.
37. Ladder Network - Keras Implementation of Ladder Network for Semi-Supervised Learning
38. toolbox: Curated list of ML libraries
39. CNN Explainer
40. AI Expert Roadmap - Roadmap to becoming an Artificial Intelligence Expert
41. Awesome Drug Interactions, Synergy, and Polypharmacy Prediction

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

## License

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