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
  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


  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


  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 160×120 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+ 1536×1024 (16 bit) calibrated outdoor images (Formats: homebrew)
  45. ICG Testhouse sequence – 2 turntable sequences from ifferent viewing heights, 36 images each, resolution 1000×750, 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 28×28 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. 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.



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