# Awesome Deep Learning [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

## Table of Contents

* **[Books](#books)**

* **[Courses](#courses)**  

* **[Videos and Lectures](#videos-and-lectures)**  

* **[Papers](#papers)**  

* **[Tutorials](#tutorials)**  

* **[Researchers](#researchers)**  

* **[Websites](#websites)**  

* **[Datasets](#datasets)**

* **[Conferences](#Conferences)**

* **[Frameworks](#frameworks)**  

* **[Tools](#tools)**  

* **[Miscellaneous](#miscellaneous)**  

* **[Contributing](#contributing)**  


### Books

1.  [Deep Learning](http://www.deeplearningbook.org/) by Yoshua Bengio, Ian Goodfellow and Aaron Courville  (05/07/2015)
2.  [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) by  Michael Nielsen (Dec 2014)
3.  [Deep Learning](http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf) by Microsoft Research (2013)
4.  [Deep Learning Tutorial](http://deeplearning.net/tutorial/deeplearning.pdf) by LISA lab, University of Montreal (Jan 6 2015)
5.  [neuraltalk](https://github.com/karpathy/neuraltalk) by Andrej Karpathy : numpy-based RNN/LSTM implementation
6.  [An introduction to genetic algorithms](http://www.boente.eti.br/fuzzy/ebook-fuzzy-mitchell.pdf)
7.  [Artificial Intelligence: A Modern Approach](http://aima.cs.berkeley.edu/)
8.  [Deep Learning in Neural Networks: An Overview](http://arxiv.org/pdf/1404.7828v4.pdf)
9.  [Artificial intelligence and machine learning: Topic wise explanation](https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/)
10. [Grokking Deep Learning for Computer Vision](https://www.manning.com/books/grokking-deep-learning-for-computer-vision)
11. [Dive into Deep Learning](https://d2l.ai/) - numpy based interactive Deep Learning book
12. [Practical Deep Learning for Cloud, Mobile, and Edge](https://www.oreilly.com/library/view/practical-deep-learning/9781492034858/) - A book for optimization techniques during production.
13. [Math and Architectures of Deep Learning](https://www.manning.com/books/math-and-architectures-of-deep-learning) - by Krishnendu Chaudhury
14. [TensorFlow 2.0 in Action](https://www.manning.com/books/tensorflow-in-action) - by Thushan Ganegedara
15. [Deep Learning for Natural Language Processing](https://www.manning.com/books/deep-learning-for-natural-language-processing) - by Stephan Raaijmakers
16. [Deep Learning Patterns and Practices](https://www.manning.com/books/deep-learning-patterns-and-practices) - by Andrew Ferlitsch
17. [Inside Deep Learning](https://www.manning.com/books/inside-deep-learning) - by Edward Raff
18. [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition) - by François Chollet
19. [Evolutionary Deep Learning](https://www.manning.com/books/evolutionary-deep-learning) - by Micheal Lanham
20. [Engineering Deep Learning Platforms](https://www.manning.com/books/engineering-deep-learning-platforms) - by Chi Wang and Donald Szeto
21. [Deep Learning with R, Second Edition](https://www.manning.com/books/deep-learning-with-r-second-edition) - by François Chollet with Tomasz Kalinowski and J. J. Allaire
22. [Regularization in Deep Learning](https://www.manning.com/books/regularization-in-deep-learning) - by Liu Peng
23. [Jax in Action](https://www.manning.com/books/jax-in-action) - by Grigory Sapunov
24. [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow](https://www.knowledgeisle.com/wp-content/uploads/2019/12/2-Aur%C3%A9lien-G%C3%A9ron-Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-Tensorflow_-Concepts-Tools-and-Techniques-to-Build-Intelligent-Systems-O%E2%80%99Reilly-Media-2019.pdf) by Aurélien Géron  | Oct 15, 2019

### Courses

1.  [Machine Learning - Stanford](https://class.coursera.org/ml-005) by Andrew Ng in Coursera (2010-2014)
2.  [Machine Learning - Caltech](http://work.caltech.edu/lectures.html) by Yaser Abu-Mostafa (2012-2014)
3.  [Machine Learning - Carnegie Mellon](http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml) by Tom Mitchell (Spring 2011)
2.  [Neural Networks for Machine Learning](https://class.coursera.org/neuralnets-2012-001) by Geoffrey Hinton in Coursera (2012)
3.  [Neural networks class](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH) by Hugo Larochelle from Université de Sherbrooke (2013)
4.  [Deep Learning Course](http://cilvr.cs.nyu.edu/doku.php?id=deeplearning:slides:start) by CILVR lab @ NYU (2014)
5.  [A.I - Berkeley](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/courseware/) by Dan Klein and Pieter Abbeel (2013)
6.  [A.I - MIT](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/) by Patrick Henry Winston (2010)
7.  [Vision and learning - computers and brains](http://web.mit.edu/course/other/i2course/www/vision_and_learning_fall_2013.html) by Shimon Ullman, Tomaso Poggio, Ethan Meyers @ MIT (2013)
9.  [Convolutional Neural Networks for Visual Recognition - Stanford](http://vision.stanford.edu/teaching/cs231n/syllabus.html) by Fei-Fei Li, Andrej Karpathy (2017)
10.  [Deep Learning for Natural Language Processing - Stanford](http://cs224d.stanford.edu/)
11.  [Neural Networks - usherbrooke](http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html)
12.  [Machine Learning - Oxford](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/) (2014-2015)
13.  [Deep Learning - Nvidia](https://developer.nvidia.com/deep-learning-courses) (2015)
14.  [Graduate Summer School: Deep Learning, Feature Learning](https://www.youtube.com/playlist?list=PLHyI3Fbmv0SdzMHAy0aN59oYnLy5vyyTA) by Geoffrey Hinton, Yoshua Bengio, Yann LeCun, Andrew Ng, Nando de Freitas and several others @ IPAM, UCLA (2012)
15.  [Deep Learning - Udacity/Google](https://www.udacity.com/course/deep-learning--ud730) by Vincent Vanhoucke and Arpan Chakraborty (2016)
16.  [Deep Learning - UWaterloo](https://www.youtube.com/playlist?list=PLehuLRPyt1Hyi78UOkMPWCGRxGcA9NVOE) by Prof. Ali Ghodsi at University of Waterloo (2015)
17.  [Statistical Machine Learning - CMU](https://www.youtube.com/watch?v=azaLcvuql_g&list=PLjbUi5mgii6BWEUZf7He6nowWvGne_Y8r) by Prof. Larry Wasserman
18.  [Deep Learning Course](https://www.college-de-france.fr/site/en-yann-lecun/course-2015-2016.htm) by Yann LeCun (2016)
19. [Designing, Visualizing and Understanding Deep Neural Networks-UC Berkeley](https://www.youtube.com/playlist?list=PLkFD6_40KJIxopmdJF_CLNqG3QuDFHQUm)
20. [UVA Deep Learning Course](http://uvadlc.github.io) MSc in Artificial Intelligence for the University of Amsterdam.
21. [MIT 6.S094: Deep Learning for Self-Driving Cars](http://selfdrivingcars.mit.edu/)
22. [MIT 6.S191: Introduction to Deep Learning](http://introtodeeplearning.com/)
23. [Berkeley CS 294: Deep Reinforcement Learning](http://rll.berkeley.edu/deeprlcourse/)
24. [Keras in Motion video course](https://www.manning.com/livevideo/keras-in-motion)
25. [Practical Deep Learning For Coders](http://course.fast.ai/) by Jeremy Howard - Fast.ai
26. [Introduction to Deep Learning](http://deeplearning.cs.cmu.edu/) by Prof. Bhiksha Raj (2017)
27. [AI for Everyone](https://www.deeplearning.ai/ai-for-everyone/) by Andrew Ng (2019)
28. [MIT Intro to Deep Learning 7 day bootcamp](https://introtodeeplearning.com) - A seven day bootcamp designed in MIT to introduce deep learning methods and applications (2019)
29. [Deep Blueberry: Deep Learning](https://mithi.github.io/deep-blueberry) - 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)
30. [Spinning Up in Deep Reinforcement Learning](https://spinningup.openai.com/) - A free deep reinforcement learning course by OpenAI (2019)
31. [Deep Learning Specialization - Coursera](https://www.coursera.org/specializations/deep-learning) - Breaking into AI with the best course from Andrew NG.
32. [Deep Learning - UC Berkeley | STAT-157](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQHBCoGaObUljoXAyyqhpFW) by Alex Smola and Mu Li (2019)
33. [Machine Learning for Mere Mortals video course](https://www.manning.com/livevideo/machine-learning-for-mere-mortals) by Nick Chase
34. [Machine Learning Crash Course with TensorFlow APIs](https://developers.google.com/machine-learning/crash-course/) -Google AI
35. [Deep Learning from the Foundations](https://course.fast.ai/part2) Jeremy Howard - Fast.ai
36. [Deep Reinforcement Learning (nanodegree) - Udacity](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893) a 3-6 month Udacity nanodegree, spanning multiple courses (2018)
37. [Grokking Deep Learning in Motion](https://www.manning.com/livevideo/grokking-deep-learning-in-motion) by Beau Carnes (2018)
38. [Face Detection with Computer Vision and Deep Learning](https://www.udemy.com/share/1000gAA0QdcV9aQng=/) by Hakan Cebeci
39. [Deep Learning Online Course list at Classpert](https://classpert.com/deep-learning) List of Deep Learning online courses (some are free) from Classpert Online Course Search
40. [AWS Machine Learning](https://aws.training/machinelearning) Machine Learning and Deep Learning Courses from Amazon's Machine Learning university
41. [Intro to Deep Learning with PyTorch](https://www.udacity.com/course/deep-learning-pytorch--ud188) - A great introductory course on Deep Learning by Udacity and Facebook AI
42. [Deep Learning by Kaggle](https://www.kaggle.com/learn/deep-learning) - Kaggle's  free course on Deep Learning
43. [Yann LeCun’s Deep Learning Course at CDS](https://cds.nyu.edu/deep-learning/) - DS-GA 1008 · SPRING 2021 
44. [Neural Networks and Deep Learning](https://webcms3.cse.unsw.edu.au/COMP9444/19T3/) - COMP9444 19T3
45. [Deep Learning A.I.Shelf](http://aishelf.org/category/ia/deep-learning/)

### Videos and Lectures

1.  [How To Create A Mind](https://www.youtube.com/watch?v=RIkxVci-R4k) By Ray Kurzweil
2.  [Deep Learning, Self-Taught Learning and Unsupervised Feature Learning](https://www.youtube.com/watch?v=n1ViNeWhC24) By Andrew Ng
3.  [Recent Developments in Deep Learning](https://www.youtube.com/watch?v=vShMxxqtDDs&amp;index=3&amp;list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT) By Geoff Hinton
4.  [The Unreasonable Effectiveness of Deep Learning](https://www.youtube.com/watch?v=sc-KbuZqGkI) by Yann LeCun
5.  [Deep Learning of Representations](https://www.youtube.com/watch?v=4xsVFLnHC_0) by Yoshua bengio
6.  [Principles of Hierarchical Temporal Memory](https://www.youtube.com/watch?v=6ufPpZDmPKA) by Jeff Hawkins
7.  [Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab](https://www.youtube.com/watch?v=2QJi0ArLq7s&amp;list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT) by Adam Coates
8.  [Making Sense of the World with Deep Learning](http://vimeo.com/80821560) By Adam Coates
9.  [Demystifying Unsupervised Feature Learning ](https://www.youtube.com/watch?v=wZfVBwOO0-k) By Adam Coates
10.  [Visual Perception with Deep Learning](https://www.youtube.com/watch?v=3boKlkPBckA) By Yann LeCun
11.  [The Next Generation of Neural Networks](https://www.youtube.com/watch?v=AyzOUbkUf3M) By Geoffrey Hinton at GoogleTechTalks
12.  [The wonderful and terrifying implications of computers that can learn](http://www.ted.com/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn) By Jeremy Howard at TEDxBrussels
13.  [Unsupervised Deep Learning - Stanford](http://web.stanford.edu/class/cs294a/handouts.html) by Andrew Ng in Stanford (2011)
14.  [Natural Language Processing](http://web.stanford.edu/class/cs224n/handouts/) By Chris Manning in Stanford
15.  [A beginners Guide to Deep Neural Networks](http://googleresearch.blogspot.com/2015/09/a-beginners-guide-to-deep-neural.html) By Natalie Hammel and Lorraine Yurshansky
16.  [Deep Learning: Intelligence from Big Data](https://www.youtube.com/watch?v=czLI3oLDe8M) by Steve Jurvetson (and panel) at VLAB in Stanford.
17. [Introduction to Artificial Neural Networks and Deep Learning](https://www.youtube.com/watch?v=FoO8qDB8gUU) by Leo Isikdogan at Motorola Mobility HQ
18. [NIPS 2016 lecture and workshop videos](https://nips.cc/Conferences/2016/Schedule) - NIPS 2016
19. [Deep Learning Crash Course](https://www.youtube.com/watch?v=oS5fz_mHVz0&list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07): a series of mini-lectures by Leo Isikdogan on YouTube (2018)
20. [Deep Learning Crash Course](https://www.manning.com/livevideo/deep-learning-crash-course) By Oliver Zeigermann
21. [Deep Learning with R in Motion](https://www.manning.com/livevideo/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](https://www.youtube.com/playlist?list=PLheiZMDg_8ufxEx9cNVcOYXsT3BppJP4b): 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](https://www.youtube.com/playlist?list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF): 2020 version 
24. [Deepmind x UCL Reinforcement Learning](https://www.youtube.com/playlist?list=PLqYmG7hTraZBKeNJ-JE_eyJHZ7XgBoAyb): Deep Reinforcement Learning
25. [CMU 11-785 Intro to Deep learning Spring 2020](https://www.youtube.com/playlist?list=PLp-0K3kfddPzCnS4CqKphh-zT3aDwybDe) Course: 11-785, Intro to Deep Learning by Bhiksha Raj 
26. [Machine Learning CS 229](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU) : End part focuses on deep learning By Andrew Ng
27. [What is Neural Structured Learning by Andrew Ferlitsch](https://youtu.be/LXWSE_9gHd0)
28. [Deep Learning Design Patterns by Andrew Ferlitsch](https://youtu.be/_DaviS6K0Vc)
29. [Architecture of a Modern CNN: the design pattern approach by Andrew Ferlitsch](https://youtu.be/QCGSS3kyGo0)
30. [Metaparameters in a CNN by Andrew Ferlitsch](https://youtu.be/K1PLeggQ33I)
31. [Multi-task CNN: a real-world example by Andrew Ferlitsch](https://youtu.be/dH2nuI-1-qM)
32. [A friendly introduction to deep reinforcement learning by Luis Serrano](https://youtu.be/1FyAh07jh0o)
33. [What are GANs and how do they work? by Edward Raff](https://youtu.be/f6ivp84qFUc)
34. [Coding a basic WGAN in PyTorch by Edward Raff](https://youtu.be/7VRdaqMDalQ)
35. [Training a Reinforcement Learning Agent by Miguel Morales](https://youtu.be/8TMT-gHlj_Q)
36. [Understand what is Deep Learning](https://www.scaler.com/topics/what-is-deep-learning/)

### Papers
*You can also find the most cited deep learning papers from [here](https://github.com/terryum/awesome-deep-learning-papers)*

1.  [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
2.  [Using Very Deep Autoencoders for Content Based Image Retrieval](http://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf)
3.  [Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf)
4.  [CMU’s list of papers](http://deeplearning.cs.cmu.edu/)
5.  [Neural Networks for Named Entity Recognition](http://nlp.stanford.edu/~socherr/pa4_ner.pdf) [zip](http://nlp.stanford.edu/~socherr/pa4-ner.zip)
6. [Training tricks by YB](http://www.iro.umontreal.ca/~bengioy/papers/YB-tricks.pdf)
7. [Geoff Hinton's reading list (all papers)](http://www.cs.toronto.edu/~hinton/deeprefs.html)
8. [Supervised Sequence Labelling with Recurrent Neural Networks](http://www.cs.toronto.edu/~graves/preprint.pdf)
9.  [Statistical Language Models based on Neural Networks](http://www.fit.vutbr.cz/~imikolov/rnnlm/thesis.pdf)
10.  [Training Recurrent Neural Networks](http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf)
11.  [Recursive Deep Learning for Natural Language Processing and Computer Vision](http://nlp.stanford.edu/~socherr/thesis.pdf)
12.  [Bi-directional RNN](http://www.di.ufpe.br/~fnj/RNA/bibliografia/BRNN.pdf)
13.  [LSTM](http://web.eecs.utk.edu/~itamar/courses/ECE-692/Bobby_paper1.pdf)
14.  [GRU - Gated Recurrent Unit](http://arxiv.org/pdf/1406.1078v3.pdf)
15.  [GFRNN](http://arxiv.org/pdf/1502.02367v3.pdf) [.](http://jmlr.org/proceedings/papers/v37/chung15.pdf) [.](http://jmlr.org/proceedings/papers/v37/chung15-supp.pdf)
16.  [LSTM: A Search Space Odyssey](http://arxiv.org/pdf/1503.04069v1.pdf)
17.  [A Critical Review of Recurrent Neural Networks for Sequence Learning](http://arxiv.org/pdf/1506.00019v1.pdf)
18.  [Visualizing and Understanding Recurrent Networks](http://arxiv.org/pdf/1506.02078v1.pdf)
19.  [Wojciech Zaremba, Ilya Sutskever, An Empirical Exploration of Recurrent Network Architectures](http://jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)
20.  [Recurrent Neural Network based Language Model](http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf)
21.  [Extensions of Recurrent Neural Network Language Model](http://www.fit.vutbr.cz/research/groups/speech/publi/2011/mikolov_icassp2011_5528.pdf)
22.  [Recurrent Neural Network based Language Modeling in Meeting Recognition](http://www.fit.vutbr.cz/~imikolov/rnnlm/ApplicationOfRNNinMeetingRecognition_IS2011.pdf)
23.  [Deep Neural Networks for Acoustic Modeling in Speech Recognition](http://cs224d.stanford.edu/papers/maas_paper.pdf)
24.  [Speech Recognition with Deep Recurrent Neural Networks](http://www.cs.toronto.edu/~fritz/absps/RNN13.pdf)
25.  [Reinforcement Learning Neural Turing Machines](http://arxiv.org/pdf/1505.00521v1)
26.  [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](http://arxiv.org/pdf/1406.1078v3.pdf)
27. [Google - Sequence to Sequence  Learning with Neural Networks](http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf)
28. [Memory Networks](http://arxiv.org/pdf/1410.3916v10)
29. [Policy Learning with Continuous Memory States for Partially Observed Robotic Control](http://arxiv.org/pdf/1507.01273v1)
30. [Microsoft - Jointly Modeling Embedding and Translation to Bridge Video and Language](http://arxiv.org/pdf/1505.01861v1.pdf)
31. [Neural Turing Machines](http://arxiv.org/pdf/1410.5401v2.pdf)
32. [Ask Me Anything: Dynamic Memory Networks for Natural Language Processing](http://arxiv.org/pdf/1506.07285v1.pdf)
33. [Mastering the Game of Go with Deep Neural Networks and Tree Search](http://www.nature.com/nature/journal/v529/n7587/pdf/nature16961.pdf)
34. [Batch Normalization](https://arxiv.org/abs/1502.03167)
35. [Residual Learning](https://arxiv.org/pdf/1512.03385v1.pdf)
36. [Image-to-Image Translation with Conditional Adversarial Networks](https://arxiv.org/pdf/1611.07004v1.pdf)
37. [Berkeley AI Research (BAIR) Laboratory](https://arxiv.org/pdf/1611.07004v1.pdf)
38. [MobileNets by Google](https://arxiv.org/abs/1704.04861)
39. [Cross Audio-Visual Recognition in the Wild Using Deep Learning](https://arxiv.org/abs/1706.05739)
40. [Dynamic Routing Between Capsules](https://arxiv.org/abs/1710.09829)
41. [Matrix Capsules With Em Routing](https://openreview.net/pdf?id=HJWLfGWRb)
42. [Efficient BackProp](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)
43. [Generative Adversarial Nets](https://arxiv.org/pdf/1406.2661v1.pdf)
44. [Fast R-CNN](https://arxiv.org/pdf/1504.08083.pdf)
45. [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://arxiv.org/pdf/1503.03832.pdf)
46. [Siamese Neural Networks for One-shot Image Recognition](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf)
47. [Unsupervised Translation of Programming Languages](https://arxiv.org/pdf/2006.03511.pdf)
48. [Matching Networks for One Shot Learning](http://papers.nips.cc/paper/6385-matching-networks-for-one-shot-learning.pdf)
49. [VOLO: Vision Outlooker for Visual Recognition](https://arxiv.org/pdf/2106.13112.pdf)
50. [ViT: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/pdf/2010.11929.pdf)
51. [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](http://proceedings.mlr.press/v37/ioffe15.pdf)
52. [DeepFaceDrawing: Deep Generation of Face Images from Sketches](http://geometrylearning.com/paper/DeepFaceDrawing.pdf?fbclid=IwAR0colWFHPGBCB1APZq9JVsWeWtmeZd9oCTNQvR52T5PRUJP_dLOwB8pt0I)

### Tutorials

1.  [UFLDL Tutorial 1](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial)
2.  [UFLDL Tutorial 2](http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/)
3.  [Deep Learning for NLP (without Magic)](http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial)
4.  [A Deep Learning Tutorial: From Perceptrons to Deep Networks](http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks)
5.  [Deep Learning from the Bottom up](http://www.metacademy.org/roadmaps/rgrosse/deep_learning)
6.  [Theano Tutorial](http://deeplearning.net/tutorial/deeplearning.pdf)
7.  [Neural Networks for Matlab](http://uk.mathworks.com/help/pdf_doc/nnet/nnet_ug.pdf)
8.  [Using convolutional neural nets to detect facial keypoints tutorial](http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/)
9.  [Torch7 Tutorials](https://github.com/clementfarabet/ipam-tutorials/tree/master/th_tutorials)
10.  [The Best Machine Learning Tutorials On The Web](https://github.com/josephmisiti/machine-learning-module)
11. [VGG Convolutional Neural Networks Practical](http://www.robots.ox.ac.uk/~vgg/practicals/cnn/index.html)
12. [TensorFlow tutorials](https://github.com/nlintz/TensorFlow-Tutorials)
13. [More TensorFlow tutorials](https://github.com/pkmital/tensorflow_tutorials)
13. [TensorFlow Python Notebooks](https://github.com/aymericdamien/TensorFlow-Examples)
14. [Keras and Lasagne Deep Learning Tutorials](https://github.com/Vict0rSch/deep_learning)
15. [Classification on raw time series in TensorFlow with a LSTM RNN](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition)
16. [Using convolutional neural nets to detect facial keypoints tutorial](http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/)
17. [TensorFlow-World](https://github.com/astorfi/TensorFlow-World)
18. [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python)
19. [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning)
20. [Deep Learning for Search](https://www.manning.com/books/deep-learning-for-search)
21. [Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder](https://medium.com/sicara/keras-tutorial-content-based-image-retrieval-convolutional-denoising-autoencoder-dc91450cc511)
22. [Pytorch Tutorial by Yunjey Choi](https://github.com/yunjey/pytorch-tutorial)
23. [Understanding deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras](https://ahmedbesbes.com/understanding-deep-convolutional-neural-networks-with-a-practical-use-case-in-tensorflow-and-keras.html)
24. [Overview and benchmark of traditional and deep learning models in text classification](https://ahmedbesbes.com/overview-and-benchmark-of-traditional-and-deep-learning-models-in-text-classification.html)
25. [Hardware for AI: Understanding computer hardware & build your own computer](https://github.com/MelAbgrall/HardwareforAI)
26. [Programming Community Curated Resources](https://hackr.io/tutorials/learn-artificial-intelligence-ai)
27. [The Illustrated Self-Supervised Learning](https://amitness.com/2020/02/illustrated-self-supervised-learning/)
28. [Visual Paper Summary: ALBERT (A Lite BERT)](https://amitness.com/2020/02/albert-visual-summary/)
28. [Semi-Supervised Deep Learning with GANs for Melanoma Detection](https://www.manning.com/liveproject/semi-supervised-deep-learning-with-gans-for-melanoma-detection/)
29. [Named Entity Recognition using Reformers](https://github.com/SauravMaheshkar/Trax-Examples/blob/main/NLP/NER%20using%20Reformer.ipynb)
30. [Deep N-Gram Models on Shakespeare’s works](https://github.com/SauravMaheshkar/Trax-Examples/blob/main/NLP/Deep%20N-Gram.ipynb)
31. [Wide Residual Networks](https://github.com/SauravMaheshkar/Trax-Examples/blob/main/vision/illustrated-wideresnet.ipynb)
32. [Fashion MNIST using Flax](https://github.com/SauravMaheshkar/Flax-Examples)
33. [Fake News Classification (with streamlit deployment)](https://github.com/SauravMaheshkar/Fake-News-Classification)
34. [Regression Analysis for Primary Biliary Cirrhosis](https://github.com/SauravMaheshkar/CoxPH-Model-for-Primary-Biliary-Cirrhosis)
35. [Cross Matching Methods for Astronomical Catalogs](https://github.com/SauravMaheshkar/Cross-Matching-Methods-for-Astronomical-Catalogs)
36. [Named Entity Recognition using BiDirectional LSTMs](https://github.com/SauravMaheshkar/Named-Entity-Recognition-)
37. [Image Recognition App using Tflite and Flutter](https://github.com/SauravMaheshkar/Flutter_Image-Recognition)

## Researchers

1. [Aaron Courville](http://aaroncourville.wordpress.com)
2. [Abdel-rahman Mohamed](http://www.cs.toronto.edu/~asamir/)
3. [Adam Coates](http://cs.stanford.edu/~acoates/)
4. [Alex Acero](http://research.microsoft.com/en-us/people/alexac/)
5. [ Alex Krizhevsky ](http://www.cs.utoronto.ca/~kriz/index.html)
6. [ Alexander Ilin ](http://users.ics.aalto.fi/alexilin/)
7. [ Amos Storkey ](http://homepages.inf.ed.ac.uk/amos/)
8. [ Andrej Karpathy ](https://karpathy.ai/)
9. [ Andrew M. Saxe ](http://www.stanford.edu/~asaxe/)
10. [ Andrew Ng ](http://www.cs.stanford.edu/people/ang/)
11. [ Andrew W. Senior ](http://research.google.com/pubs/author37792.html)
12. [ Andriy Mnih ](http://www.gatsby.ucl.ac.uk/~amnih/)
13. [ Ayse Naz Erkan ](http://www.cs.nyu.edu/~naz/)
14. [ Benjamin Schrauwen ](http://reslab.elis.ugent.be/benjamin)
15. [ Bernardete Ribeiro ](https://www.cisuc.uc.pt/people/show/2020)
16. [ Bo David Chen ](http://vision.caltech.edu/~bchen3/Site/Bo_David_Chen.html)
17. [ Boureau Y-Lan ](http://cs.nyu.edu/~ylan/)
18. [ Brian Kingsbury ](http://researcher.watson.ibm.com/researcher/view.php?person=us-bedk)
19. [ Christopher Manning ](http://nlp.stanford.edu/~manning/)
20. [ Clement Farabet ](http://www.clement.farabet.net/)
21. [ Dan Claudiu Cireșan ](http://www.idsia.ch/~ciresan/)
22. [ David Reichert ](http://serre-lab.clps.brown.edu/person/david-reichert/)
23. [ Derek Rose ](http://mil.engr.utk.edu/nmil/member/5.html)
24. [ Dong Yu ](http://research.microsoft.com/en-us/people/dongyu/default.aspx)
25. [ Drausin Wulsin ](http://www.seas.upenn.edu/~wulsin/)
26. [ Erik M. Schmidt ](http://music.ece.drexel.edu/people/eschmidt)
27. [ Eugenio Culurciello ](https://engineering.purdue.edu/BME/People/viewPersonById?resource_id=71333)
28. [ Frank Seide ](http://research.microsoft.com/en-us/people/fseide/)
29. [ Galen Andrew ](http://homes.cs.washington.edu/~galen/)
30. [ Geoffrey Hinton ](http://www.cs.toronto.edu/~hinton/)
31. [ George Dahl ](http://www.cs.toronto.edu/~gdahl/)
32. [ Graham Taylor ](http://www.uoguelph.ca/~gwtaylor/)
33. [ Grégoire Montavon ](http://gregoire.montavon.name/)
34. [ Guido Francisco Montúfar ](http://personal-homepages.mis.mpg.de/montufar/)
35. [ Guillaume Desjardins ](http://brainlogging.wordpress.com/)
36. [ Hannes Schulz ](http://www.ais.uni-bonn.de/~schulz/)
37. [ Hélène Paugam-Moisy ](http://www.lri.fr/~hpaugam/)
38. [ Honglak Lee ](http://web.eecs.umich.edu/~honglak/)
39. [ Hugo Larochelle ](http://www.dmi.usherb.ca/~larocheh/index_en.html)
40. [ Ilya Sutskever ](http://www.cs.toronto.edu/~ilya/)
41. [ Itamar Arel ](http://mil.engr.utk.edu/nmil/member/2.html)
42. [ James Martens ](http://www.cs.toronto.edu/~jmartens/)
43. [ Jason Morton ](http://www.jasonmorton.com/)
44. [ Jason Weston ](http://www.thespermwhale.com/jaseweston/)
45. [ Jeff Dean ](http://research.google.com/pubs/jeff.html)
46. [ Jiquan Mgiam ](http://cs.stanford.edu/~jngiam/)
47. [ Joseph Turian ](http://www-etud.iro.umontreal.ca/~turian/)
48. [ Joshua Matthew Susskind ](http://aclab.ca/users/josh/index.html)
49. [ Jürgen Schmidhuber ](http://www.idsia.ch/~juergen/)
50. [ Justin A. Blanco ](https://sites.google.com/site/blancousna/)
51. [ Koray Kavukcuoglu ](http://koray.kavukcuoglu.org/)
52. [ KyungHyun Cho ](http://users.ics.aalto.fi/kcho/)
53. [ Li Deng ](http://research.microsoft.com/en-us/people/deng/)
54. [ Lucas Theis ](http://www.kyb.tuebingen.mpg.de/nc/employee/details/lucas.html)
55. [ Ludovic Arnold ](http://ludovicarnold.altervista.org/home/)
56. [ Marc'Aurelio Ranzato ](http://www.cs.nyu.edu/~ranzato/)
57. [ Martin Längkvist ](http://aass.oru.se/~mlt/)
58. [ Misha Denil ](http://mdenil.com/)
59. [ Mohammad Norouzi ](http://www.cs.toronto.edu/~norouzi/)
60. [ Nando de Freitas ](http://www.cs.ubc.ca/~nando/)
61. [ Navdeep Jaitly ](http://www.cs.utoronto.ca/~ndjaitly/)
62. [ Nicolas Le Roux ](http://nicolas.le-roux.name/)
63. [ Nitish Srivastava ](http://www.cs.toronto.edu/~nitish/)
64. [ Noel Lopes ](https://www.cisuc.uc.pt/people/show/2028)
65. [ Oriol Vinyals ](http://www.cs.berkeley.edu/~vinyals/)
66. [ Pascal Vincent ](http://www.iro.umontreal.ca/~vincentp)
67. [ Patrick Nguyen ](https://sites.google.com/site/drpngx/)
68. [ Pedro Domingos ](http://homes.cs.washington.edu/~pedrod/)
69. [ Peggy Series ](http://homepages.inf.ed.ac.uk/pseries/)
70. [ Pierre Sermanet ](http://cs.nyu.edu/~sermanet)
71. [ Piotr Mirowski ](http://www.cs.nyu.edu/~mirowski/)
72. [ Quoc V. Le ](http://ai.stanford.edu/~quocle/)
73. [ Reinhold Scherer ](http://bci.tugraz.at/scherer/)
74. [ Richard Socher ](http://www.socher.org/)
75. [ Rob Fergus ](http://cs.nyu.edu/~fergus/pmwiki/pmwiki.php)
76. [ Robert Coop ](http://mil.engr.utk.edu/nmil/member/19.html)
77. [ Robert Gens ](http://homes.cs.washington.edu/~rcg/)
78. [ Roger Grosse ](http://people.csail.mit.edu/rgrosse/)
79. [ Ronan Collobert ](http://ronan.collobert.com/)
80. [ Ruslan Salakhutdinov ](http://www.utstat.toronto.edu/~rsalakhu/)
81. [ Sebastian Gerwinn ](http://www.kyb.tuebingen.mpg.de/nc/employee/details/sgerwinn.html)
82. [ Stéphane Mallat ](http://www.cmap.polytechnique.fr/~mallat/)
83. [ Sven Behnke ](http://www.ais.uni-bonn.de/behnke/)
84. [ Tapani Raiko ](http://users.ics.aalto.fi/praiko/)
85. [ Tara Sainath ](https://sites.google.com/site/tsainath/)
86. [ Tijmen Tieleman ](http://www.cs.toronto.edu/~tijmen/)
87. [ Tom Karnowski ](http://mil.engr.utk.edu/nmil/member/36.html)
88. [ Tomáš Mikolov ](https://research.facebook.com/tomas-mikolov)
89. [ Ueli Meier ](http://www.idsia.ch/~meier/)
90. [ Vincent Vanhoucke ](http://vincent.vanhoucke.com)
91. [ Volodymyr Mnih ](http://www.cs.toronto.edu/~vmnih/)
92. [ Yann LeCun ](http://yann.lecun.com/)
93. [ Yichuan Tang ](http://www.cs.toronto.edu/~tang/)
94. [ Yoshua Bengio ](http://www.iro.umontreal.ca/~bengioy/yoshua_en/index.html)
95. [ Yotaro Kubo ](http://yota.ro/)
96. [ Youzhi (Will) Zou ](http://ai.stanford.edu/~wzou)
97. [ Fei-Fei Li ](http://vision.stanford.edu/feifeili)
98. [ Ian Goodfellow ](https://research.google.com/pubs/105214.html)
99. [ Robert Laganière ](http://www.site.uottawa.ca/~laganier/)
100. [Merve Ayyüce Kızrak](http://www.ayyucekizrak.com/)


### Websites

1.  [deeplearning.net](http://deeplearning.net/)
2.  [deeplearning.stanford.edu](http://deeplearning.stanford.edu/)
3.  [nlp.stanford.edu](http://nlp.stanford.edu/)
4.  [ai-junkie.com](http://www.ai-junkie.com/ann/evolved/nnt1.html)
5.  [cs.brown.edu/research/ai](http://cs.brown.edu/research/ai/)
6.  [eecs.umich.edu/ai](http://www.eecs.umich.edu/ai/)
7.  [cs.utexas.edu/users/ai-lab](http://www.cs.utexas.edu/users/ai-lab/)
8.  [cs.washington.edu/research/ai](http://www.cs.washington.edu/research/ai/)
9.  [aiai.ed.ac.uk](http://www.aiai.ed.ac.uk/)
10.  [www-aig.jpl.nasa.gov](http://www-aig.jpl.nasa.gov/)
11.  [csail.mit.edu](http://www.csail.mit.edu/)
12.  [cgi.cse.unsw.edu.au/~aishare](http://cgi.cse.unsw.edu.au/~aishare/)
13.  [cs.rochester.edu/research/ai](http://www.cs.rochester.edu/research/ai/)
14.  [ai.sri.com](http://www.ai.sri.com/)
15.  [isi.edu/AI/isd.htm](http://www.isi.edu/AI/isd.htm)
16.  [nrl.navy.mil/itd/aic](http://www.nrl.navy.mil/itd/aic/)
17.  [hips.seas.harvard.edu](http://hips.seas.harvard.edu/)
18.  [AI Weekly](http://aiweekly.co)
19.  [stat.ucla.edu](http://statistics.ucla.edu/)
20.  [deeplearning.cs.toronto.edu](http://deeplearning.cs.toronto.edu/i2t)
21.  [jeffdonahue.com/lrcn/](http://jeffdonahue.com/lrcn/)
22.  [visualqa.org](http://www.visualqa.org/)
23.  [www.mpi-inf.mpg.de/departments/computer-vision...](https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/)
24.  [Deep Learning News](http://news.startup.ml/)
25.  [Machine Learning is Fun! Adam Geitgey's Blog](https://medium.com/@ageitgey/)
26.  [Guide to Machine Learning](http://yerevann.com/a-guide-to-deep-learning/)
27.  [Deep Learning for Beginners](https://spandan-madan.github.io/DeepLearningProject/)
28.  [Machine Learning Mastery blog](https://machinelearningmastery.com/blog/)
29.  [ML Compiled](https://ml-compiled.readthedocs.io/en/latest/)
30.  [Programming Community Curated Resources](https://hackr.io/tutorials/learn-artificial-intelligence-ai)
31.  [A Beginner's Guide To Understanding Convolutional Neural Networks](https://adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/)
32.  [ahmedbesbes.com](http://ahmedbesbes.com)
33.  [amitness.com](https://amitness.com/)
34.  [AI Summer](https://theaisummer.com/)
35.  [AI Hub - supported by AAAI, NeurIPS](https://aihub.org/)
36.  [CatalyzeX: Machine Learning Hub for Builders and Makers](https://www.catalyzeX.com)
37.  [The Epic Code](https://theepiccode.com/)
38.  [all AI news](https://allainews.com/)

### Datasets

1.  [MNIST](http://yann.lecun.com/exdb/mnist/) Handwritten digits
2.  [Google House Numbers](http://ufldl.stanford.edu/housenumbers/) from street view
3.  [CIFAR-10 and CIFAR-100](http://www.cs.toronto.edu/~kriz/cifar.html)
4.  [IMAGENET](http://www.image-net.org/)
5.  [Tiny Images](http://groups.csail.mit.edu/vision/TinyImages/) 80 Million tiny images6.  
6.  [Flickr Data](https://yahooresearch.tumblr.com/post/89783581601/one-hundred-million-creative-commons-flickr-images) 100 Million Yahoo dataset
7.  [Berkeley Segmentation Dataset 500](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/)
8.  [UC Irvine Machine Learning Repository](http://archive.ics.uci.edu/ml/)
9.  [Flickr 8k](http://nlp.cs.illinois.edu/HockenmaierGroup/Framing_Image_Description/KCCA.html)
10. [Flickr 30k](http://shannon.cs.illinois.edu/DenotationGraph/)
11. [Microsoft COCO](http://mscoco.org/home/)
12. [VQA](http://www.visualqa.org/)
13. [Image QA](http://www.cs.toronto.edu/~mren/imageqa/data/cocoqa/)
14. [AT&T Laboratories Cambridge face database](http://www.uk.research.att.com/facedatabase.html)
15. [AVHRR Pathfinder](http://xtreme.gsfc.nasa.gov)
16. [Air Freight](http://www.anc.ed.ac.uk/~amos/afreightdata.html) - 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](http://www.science.uva.nl/~aloi/) - 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](http://www.imm.dtu.dk/~aam/) - Most images & annotations are supplemented by various ASM/AAM analyses using the AAM-API. (Formats: bmp,asf)
19. [Image Analysis and Computer Graphics](http://www.imm.dtu.dk/image/)  
21. [Brown University Stimuli](http://www.cog.brown.edu/~tarr/stimuli.html) - A variety of datasets including geons, objects, and "greebles". Good for testing recognition algorithms. (Formats: pict)
22. [CAVIAR video sequences of mall and public space behavior](http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/) - 90K video frames in 90 sequences of various human activities, with XML ground truth of detection and behavior classification (Formats: MPEG2 & JPEG)
23. [Machine Vision Unit](http://www.ipab.inf.ed.ac.uk/mvu/)
25. [CCITT Fax standard images](http://www.cs.waikato.ac.nz/~singlis/ccitt.html) - 8 images (Formats: gif)
26. [CMU CIL's Stereo Data with Ground Truth](cil-ster.html) - 3 sets of 11 images, including color tiff images with spectroradiometry (Formats: gif, tiff)
27. [CMU PIE Database](http://www.ri.cmu.edu/projects/project_418.html) - A database of 41,368 face images of 68 people captured under 13 poses, 43 illuminations conditions, and with 4 different expressions.
28. [CMU VASC Image Database](http://www.ius.cs.cmu.edu/idb/) - Images, sequences, stereo pairs (thousands of images) (Formats: Sun Rasterimage)
29. [Caltech Image Database](http://www.vision.caltech.edu/html-files/archive.html) - about 20 images - mostly top-down views of small objects and toys. (Formats: GIF)
30. [Columbia-Utrecht Reflectance and Texture Database](http://www.cs.columbia.edu/CAVE/curet/) - Texture and reflectance measurements for over 60 samples of 3D texture, observed with over 200 different combinations of viewing and illumination directions. (Formats: bmp)
31. [Computational Colour Constancy Data](http://www.cs.sfu.ca/~colour/data/index.html) - 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)
32. [Computational Vision Lab](http://www.cs.sfu.ca/~colour/)
34. [Content-based image retrieval database](http://www.cs.washington.edu/research/imagedatabase/groundtruth/) - 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)
35. [Efficient Content-based Retrieval Group](http://www.cs.washington.edu/research/imagedatabase/)
37. [Densely Sampled View Spheres](http://ls7-www.cs.uni-dortmund.de/~peters/pages/research/modeladaptsys/modeladaptsys_vba_rov.html) - Densely sampled view spheres - upper half of the view sphere of two toy objects with 2500 images each. (Formats: tiff)
38. [Computer Science VII (Graphical Systems)](http://ls7-www.cs.uni-dortmund.de/)
40. [Digital Embryos](https://web-beta.archive.org/web/20011216051535/vision.psych.umn.edu/www/kersten-lab/demos/digitalembryo.html) - 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)
41. [Univerity of Minnesota Vision Lab](http://vision.psych.umn.edu/users/kersten//kersten-lab/kersten-lab.html) 
42. [El Salvador Atlas of Gastrointestinal VideoEndoscopy](http://www.gastrointestinalatlas.com) - Images and Videos of his-res of studies taken from Gastrointestinal Video endoscopy. (Formats: jpg, mpg, gif)
43. [FG-NET Facial Aging Database](http://sting.cycollege.ac.cy/~alanitis/fgnetaging/index.htm) - Database contains 1002 face images showing subjects at different ages. (Formats: jpg)
44. [FVC2000 Fingerprint Databases](http://bias.csr.unibo.it/fvc2000/) - FVC2000 is the First International Competition for Fingerprint Verification Algorithms. Four fingerprint databases constitute the FVC2000 benchmark (3520 fingerprints in all).
45. [Biometric Systems Lab](http://biolab.csr.unibo.it/home.asp) - University of Bologna
46. [Face and Gesture images and image sequences](http://www.fg-net.org) - Several image datasets of faces and gestures that are ground truth annotated for benchmarking
47. [German Fingerspelling Database](http://www-i6.informatik.rwth-aachen.de/~dreuw/database.html) - 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)  
48. [Language Processing and Pattern Recognition](http://www-i6.informatik.rwth-aachen.de/)
50. [Groningen Natural Image Database](http://hlab.phys.rug.nl/archive.html) - 4000+ 1536x1024 (16 bit) calibrated outdoor images (Formats: homebrew)
51. [ICG Testhouse sequence](http://www.icg.tu-graz.ac.at/~schindler/Data) -  2 turntable sequences from different viewing heights, 36 images each, resolution 1000x750, color (Formats: PPM)
52. [Institute of Computer Graphics and Vision](http://www.icg.tu-graz.ac.at)
54. [IEN Image Library](http://www.ien.it/is/vislib/) - 1000+ images, mostly outdoor sequences (Formats: raw, ppm)  
55. [INRIA's Syntim images database](http://www-rocq.inria.fr/~tarel/syntim/images.html) - 15 color image of simple objects (Formats: gif)
56. [INRIA](http://www.inria.fr/)
57. [INRIA's Syntim stereo databases](http://www-rocq.inria.fr/~tarel/syntim/paires.html) - 34 calibrated color stereo pairs (Formats: gif)
58. [Image Analysis Laboratory](http://www.ece.ncsu.edu/imaging/Archives/ImageDataBase/index.html) - Images obtained from a variety of imaging modalities -- raw CFA images, range images and a host of "medical images". (Formats: homebrew)
59. [Image Analysis Laboratory](http://www.ece.ncsu.edu/imaging)
61. [Image Database](http://www.prip.tuwien.ac.at/prip/image.html) - An image database including some textures  
62. [JAFFE Facial Expression Image Database](http://www.mis.atr.co.jp/~mlyons/jaffe.html) - 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.)
63. [ATR Research, Kyoto, Japan](http://www.mic.atr.co.jp/)
64. [JISCT Stereo Evaluation](ftp://ftp.vislist.com/IMAGERY/JISCT/) - 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)
65. [MIT Vision Texture](https://vismod.media.mit.edu/vismod/imagery/VisionTexture/vistex.html) - Image archive (100+ images) (Formats: ppm)
66. [MIT face images and more](ftp://whitechapel.media.mit.edu/pub/images) - hundreds of images (Formats: homebrew)
67. [Machine Vision](http://vision.cse.psu.edu/book/testbed/images/) - Images from the textbook by Jain, Kasturi, Schunck (20+ images) (Formats: GIF TIFF)
68. [Mammography Image Databases](http://marathon.csee.usf.edu/Mammography/Database.html) - 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)
69. [ftp://ftp.cps.msu.edu/pub/prip](ftp://ftp.cps.msu.edu/pub/prip) - many images (Formats: unknown)
70. [Middlebury Stereo Data Sets with Ground Truth](http://www.middlebury.edu/stereo/data.html) - 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)
71. [Middlebury Stereo Vision Research Page](http://www.middlebury.edu/stereo) - Middlebury College
72. [Modis Airborne simulator, Gallery and data set](http://ltpwww.gsfc.nasa.gov/MODIS/MAS/) - High Altitude Imagery from around the world for environmental modeling in support of NASA EOS program (Formats: JPG and HDF)
73. [NIST Fingerprint and handwriting](ftp://sequoyah.ncsl.nist.gov/pub/databases/data) - datasets - thousands of images (Formats: unknown)
74. [NIST Fingerprint data](ftp://ftp.cs.columbia.edu/jpeg/other/uuencoded) - compressed multipart uuencoded tar file
75. [NLM HyperDoc Visible Human Project](http://www.nlm.nih.gov/research/visible/visible_human.html) - Color, CAT and MRI image samples - over 30 images (Formats: jpeg)
76. [National Design Repository](http://www.designrepository.org) - Over 55,000 3D CAD and solid models of (mostly) mechanical/machined engineering designs. (Formats: gif,vrml,wrl,stp,sat) 
77. [Geometric & Intelligent Computing Laboratory](http://gicl.mcs.drexel.edu)
79. [OSU (MSU) 3D Object Model Database](http://eewww.eng.ohio-state.edu/~flynn/3DDB/Models/) - several sets of 3D object models collected over several years to use in object recognition research (Formats: homebrew, vrml)
80. [OSU (MSU/WSU) Range Image Database](http://eewww.eng.ohio-state.edu/~flynn/3DDB/RID/) - Hundreds of real and synthetic images (Formats: gif, homebrew)
81. [OSU/SAMPL Database: Range Images, 3D Models, Stills, Motion Sequences](http://sampl.eng.ohio-state.edu/~sampl/database.htm) - Over 1000 range images, 3D object models, still images and motion sequences (Formats: gif, ppm, vrml, homebrew)
82. [Signal Analysis and Machine Perception Laboratory](http://sampl.eng.ohio-state.edu)
84. [Otago Optical Flow Evaluation Sequences](http://www.cs.otago.ac.nz/research/vision/Research/OpticalFlow/opticalflow.html) - 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)
85. [Vision Research Group](http://www.cs.otago.ac.nz/research/vision/index.html)
87. [ftp://ftp.limsi.fr/pub/quenot/opflow/testdata/piv/](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))
88. [LIMSI-CNRS/CHM/IMM/vision](http://www.limsi.fr/Recherche/IMM/PageIMM.html)
89. [LIMSI-CNRS](http://www.limsi.fr/)
90. [Photometric 3D Surface Texture Database](http://www.taurusstudio.net/research/pmtexdb/index.htm) - 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)
91. [SEQUENCES FOR OPTICAL FLOW ANALYSIS (SOFA)](http://www.cee.hw.ac.uk/~mtc/sofa) - 9 synthetic sequences designed for testing motion analysis applications, including full ground truth of motion and camera parameters. (Formats: gif)
92. [Computer Vision Group](http://www.cee.hw.ac.uk/~mtc/research.html)
94. [Sequences for Flow Based Reconstruction](http://www.nada.kth.se/~zucch/CAMERA/PUB/seq.html) - synthetic sequence for testing structure from motion algorithms (Formats: pgm)
95. [Stereo Images with Ground Truth Disparity and Occlusion](http://www-dbv.cs.uni-bonn.de/stereo_data/) - 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)
96. [Stuttgart Range Image Database](http://range.informatik.uni-stuttgart.de) - A collection of synthetic range images taken from high-resolution polygonal models available on the web (Formats: homebrew)
97. [Department Image Understanding](http://www.informatik.uni-stuttgart.de/ipvr/bv/bv_home_engl.html)
99. [The AR Face Database](http://www2.ece.ohio-state.edu/~aleix/ARdatabase.html) - 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))
100. [Purdue Robot Vision Lab](http://rvl.www.ecn.purdue.edu/RVL/)
101. [The MIT-CSAIL Database of Objects and Scenes](http://web.mit.edu/torralba/www/database.html) - 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)
102. [The RVL SPEC-DB (SPECularity DataBase)](http://rvl1.ecn.purdue.edu/RVL/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 )
103. [Robot Vision Laboratory](http://rvl1.ecn.purdue.edu/RVL/)
105. [The Xm2vts database](http://xm2vtsdb.ee.surrey.ac.uk) - 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.
106. [Centre for Vision, Speech and Signal Processing](http://www.ee.surrey.ac.uk/Research/CVSSP)
107. [Traffic Image Sequences and 'Marbled Block' Sequence](http://i21www.ira.uka.de/image_sequences) - thousands of frames of digitized traffic image sequences as well as the 'Marbled Block' sequence (grayscale images) (Formats: GIF)
108. [IAKS/KOGS](http://i21www.ira.uka.de)
110. [U Bern Face images](ftp://ftp.iam.unibe.ch/pub/Images/FaceImages) - hundreds of images (Formats: Sun rasterfile)
111. [U Michigan textures](ftp://freebie.engin.umich.edu/pub/misc/textures) (Formats: compressed raw)
112. [U Oulu wood and knots database](http://www.ee.oulu.fi/~olli/Projects/Lumber.Grading.html) - Includes classifications - 1000+ color images (Formats: ppm)
113. [UCID - an Uncompressed Colour Image Database](http://vision.doc.ntu.ac.uk/datasets/UCID/ucid.html) - a benchmark database for image retrieval with predefined ground truth. (Formats: tiff)
115. [UMass Vision Image Archive](http://vis-www.cs.umass.edu/~vislib/) - Large image database with aerial, space, stereo, medical images and more. (Formats: homebrew)
116. [UNC's 3D image database](ftp://sunsite.unc.edu/pub/academic/computer-science/virtual-reality/3d) - many images (Formats: GIF)
117. [USF Range Image Data with Segmentation Ground Truth](http://marathon.csee.usf.edu/range/seg-comp/SegComp.html) - 80 image sets (Formats: Sun rasterimage)
118. [University of Oulu Physics-based Face Database](http://www.ee.oulu.fi/research/imag/color/pbfd.html) - contains color images of faces under different illuminants and camera calibration conditions as well as skin spectral reflectance measurements of each person.
119. [Machine Vision and Media Processing Unit](http://www.ee.oulu.fi/mvmp/)
121. [University of Oulu Texture Database](http://www.outex.oulu.fi) - 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)
122. [Machine Vision Group](http://www.ee.oulu.fi/mvg)
124. [Usenix face database](ftp://ftp.uu.net/published/usenix/faces) - Thousands of face images from many different sites (circa 994)
125. [View Sphere Database](http://www-prima.inrialpes.fr/Prima/hall/view_sphere.html) - 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)
126. [PRIMA, GRAVIR](http://www-prima.inrialpes.fr/Prima/)
127. [Vision-list Imagery Archive](ftp://ftp.vislist.com/IMAGERY/) - Many images, many formats
128. [Wiry Object Recognition Database](http://www.cs.cmu.edu/~owenc/word.htm) - Thousands of images of a cart, ladder, stool, bicycle, chairs, and cluttered scenes with ground truth labelings of edges and regions. (Formats: jpg)
129. [3D Vision Group](http://www.cs.cmu.edu/0.000000E+003dvision/)
131. [Yale Face Database](http://cvc.yale.edu/projects/yalefaces/yalefaces.html) -  165 images (15 individuals) with different lighting, expression, and occlusion configurations.
132. [Yale Face Database B](http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html) - 5760 single light source images of 10 subjects each seen under 576 viewing conditions (9 poses x 64 illumination conditions). (Formats: PGM)
133. [Center for Computational Vision and Control](http://cvc.yale.edu/)
134. [DeepMind QA Corpus](https://github.com/deepmind/rc-data) - Textual QA corpus from CNN and DailyMail. More than 300K documents in total. [Paper](http://arxiv.org/abs/1506.03340) for reference.
135. [YouTube-8M Dataset](https://research.google.com/youtube8m/) - 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.
136. [Open Images dataset](https://github.com/openimages/dataset) - Open Images is a dataset of ~9 million URLs to images that have been annotated with labels spanning over 6000 categories.
137. [Visual Object Classes Challenge 2012 (VOC2012)](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit) - VOC2012 dataset containing 12k images with 20 annotated classes for object detection and segmentation.
138. [Fashion-MNIST](https://github.com/zalandoresearch/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.
139. [Large-scale Fashion (DeepFashion) Database](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html) - 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
140. [FakeNewsCorpus](https://github.com/several27/FakeNewsCorpus) - Contains about 10 million news articles classified using [opensources.co](http://opensources.co) types
141. [LLVIP](https://github.com/bupt-ai-cz/LLVIP) - 15488 visible-infrared paired images (30976 images) for low-light vision research, [Project_Page](https://bupt-ai-cz.github.io/LLVIP/)
142. [MSDA](https://github.com/bupt-ai-cz/Meta-SelfLearning) - Over over 5 million images from 5 different domains for multi-source ocr/text recognition DA research, [Project_Page](https://bupt-ai-cz.github.io/Meta-SelfLearning/)
143. [SANAD: Single-Label Arabic News Articles Dataset for Automatic Text Categorization](https://data.mendeley.com/datasets/57zpx667y9/2) - 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. 
144. [Referit3D](https://referit3d.github.io) - 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.
145. [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) - Stanford released ~100,000 English QA pairs and ~50,000 unanswerable questions
146. [FQuAD](https://fquad.illuin.tech/) - ~25,000 French QA pairs released by Illuin Technology
147. [GermanQuAD and GermanDPR](https://www.deepset.ai/germanquad) - deepset released ~14,000 German QA pairs
148. [SberQuAD](https://github.com/annnyway/QA-for-Russian) - Sberbank released ~90,000 Russian QA pairs
149. [ArtEmis](http://artemisdataset.org/) - 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](http://cvpr2018.thecvf.com)
2. [AAMAS - International Joint Conference on Autonomous Agents and Multiagent Systems](http://celweb.vuse.vanderbilt.edu/aamas18/)
3. [IJCAI - 	International Joint Conference on Artificial Intelligence](https://www.ijcai-18.org/)
4. [ICML - 	International Conference on Machine Learning](https://icml.cc)
5. [ECML - European Conference on Machine Learning](http://www.ecmlpkdd2018.org)
6. [KDD - Knowledge Discovery and Data Mining](http://www.kdd.org/kdd2018/)
7. [NIPS - Neural Information Processing Systems](https://nips.cc/Conferences/2018)
8. [O'Reilly AI Conference - 	O'Reilly Artificial Intelligence Conference](https://conferences.oreilly.com/artificial-intelligence/ai-ny)
9. [ICDM - International Conference on Data Mining](https://www.waset.org/conference/2018/07/istanbul/ICDM)
10. [ICCV - International Conference on Computer Vision](http://iccv2017.thecvf.com)
11. [AAAI - Association for the Advancement of Artificial Intelligence](https://www.aaai.org)
12. [MAIS - Montreal AI Symposium](https://montrealaisymposium.wordpress.com/)

### Frameworks

1.  [Caffe](http://caffe.berkeleyvision.org/)  
2.  [Torch7](http://torch.ch/)
3.  [Theano](http://deeplearning.net/software/theano/)
4.  [cuda-convnet](https://code.google.com/p/cuda-convnet2/)
5.  [convetjs](https://github.com/karpathy/convnetjs)
5.  [Ccv](http://libccv.org/doc/doc-convnet/)
6.  [NuPIC](http://numenta.org/nupic.html)
7.  [DeepLearning4J](http://deeplearning4j.org/)
8.  [Brain](https://github.com/harthur/brain)
9.  [DeepLearnToolbox](https://github.com/rasmusbergpalm/DeepLearnToolbox)
10.  [Deepnet](https://github.com/nitishsrivastava/deepnet)
11.  [Deeppy](https://github.com/andersbll/deeppy)
12.  [JavaNN](https://github.com/ivan-vasilev/neuralnetworks)
13.  [hebel](https://github.com/hannes-brt/hebel)
14.  [Mocha.jl](https://github.com/pluskid/Mocha.jl)
15.  [OpenDL](https://github.com/guoding83128/OpenDL)
16.  [cuDNN](https://developer.nvidia.com/cuDNN)
17.  [MGL](http://melisgl.github.io/mgl-pax-world/mgl-manual.html)
18.  [Knet.jl](https://github.com/denizyuret/Knet.jl)
19.  [Nvidia DIGITS - a web app based on Caffe](https://github.com/NVIDIA/DIGITS)
20.  [Neon - Python based Deep Learning Framework](https://github.com/NervanaSystems/neon)
21.  [Keras - Theano based Deep Learning Library](http://keras.io)
22.  [Chainer - A flexible framework of neural networks for deep learning](http://chainer.org/)
23.  [RNNLM Toolkit](http://rnnlm.org/)
24.  [RNNLIB - A recurrent neural network library](http://sourceforge.net/p/rnnl/wiki/Home/)
25.  [char-rnn](https://github.com/karpathy/char-rnn)
26.  [MatConvNet: CNNs for MATLAB](https://github.com/vlfeat/matconvnet)
27.  [Minerva - a fast and flexible tool for deep learning on multi-GPU](https://github.com/dmlc/minerva)
28.  [Brainstorm - Fast, flexible and fun neural networks.](https://github.com/IDSIA/brainstorm)
29.  [Tensorflow - Open source software library for numerical computation using data flow graphs](https://github.com/tensorflow/tensorflow)
30.  [DMTK - Microsoft Distributed Machine Learning Tookit](https://github.com/Microsoft/DMTK)
31.  [Scikit Flow - Simplified interface for TensorFlow (mimicking Scikit Learn)](https://github.com/google/skflow)
32.  [MXnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning framework](https://github.com/apache/incubator-mxnet)
33.  [Veles - Samsung Distributed machine learning platform](https://github.com/Samsung/veles)
34.  [Marvin - A Minimalist GPU-only N-Dimensional ConvNets Framework](https://github.com/PrincetonVision/marvin)
35.  [Apache SINGA - A General Distributed Deep Learning Platform](http://singa.incubator.apache.org/)
36.  [DSSTNE - Amazon's library for building Deep Learning models](https://github.com/amznlabs/amazon-dsstne)
37.  [SyntaxNet - Google's syntactic parser - A TensorFlow dependency library](https://github.com/tensorflow/models/tree/master/syntaxnet)
38.  [mlpack - A scalable Machine Learning library](http://mlpack.org/)
39.  [Torchnet - Torch based Deep Learning Library](https://github.com/torchnet/torchnet)
40.  [Paddle - PArallel Distributed Deep LEarning by Baidu](https://github.com/baidu/paddle)
41.  [NeuPy - Theano based Python library for ANN and Deep Learning](http://neupy.com)
42.  [Lasagne - a lightweight library to build and train neural networks in Theano](https://github.com/Lasagne/Lasagne)
43.  [nolearn - wrappers and abstractions around existing neural network libraries, most notably Lasagne](https://github.com/dnouri/nolearn)
44.  [Sonnet - a library for constructing neural networks by Google's DeepMind](https://github.com/deepmind/sonnet)
45.  [PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration](https://github.com/pytorch/pytorch)
46.  [CNTK - Microsoft Cognitive Toolkit](https://github.com/Microsoft/CNTK)
47.  [Serpent.AI - Game agent framework: Use any video game as a deep learning sandbox](https://github.com/SerpentAI/SerpentAI)
48.  [Caffe2 - A New Lightweight, Modular, and Scalable Deep Learning Framework](https://github.com/caffe2/caffe2)
49.  [deeplearn.js - Hardware-accelerated deep learning and linear algebra (NumPy) library for the web](https://github.com/PAIR-code/deeplearnjs)
50.  [TVM - End to End Deep Learning Compiler Stack for CPUs, GPUs and specialized accelerators](https://tvm.ai/)
51.  [Coach - Reinforcement Learning Coach by Intel® AI Lab](https://github.com/NervanaSystems/coach)
52.  [albumentations - A fast and framework agnostic image augmentation library](https://github.com/albu/albumentations)
53.  [Neuraxle - A general-purpose ML pipelining framework](https://github.com/Neuraxio/Neuraxle)
54.  [Catalyst: High-level utils for PyTorch DL & RL research. It was developed with a focus on reproducibility, fast experimentation and code/ideas reusing](https://github.com/catalyst-team/catalyst)
55.  [garage - A toolkit for reproducible reinforcement learning research](https://github.com/rlworkgroup/garage)
56.  [Detecto - Train and run object detection models with 5-10 lines of code](https://github.com/alankbi/detecto)
57.  [Karate Club - An unsupervised machine learning library for graph structured data](https://github.com/benedekrozemberczki/karateclub)
58.  [Synapses - A lightweight library for neural networks that runs anywhere](https://github.com/mrdimosthenis/Synapses)
59.  [TensorForce - A TensorFlow library for applied reinforcement learning](https://github.com/reinforceio/tensorforce)
60.  [Hopsworks - A Feature Store for ML and Data-Intensive AI](https://github.com/logicalclocks/hopsworks)
61.  [Feast - A Feature Store for ML for GCP by Gojek/Google](https://github.com/gojek/feast)
62.  [PyTorch Geometric Temporal - Representation learning on dynamic graphs](https://github.com/gojek/feast)
63.  [lightly - A computer vision framework for self-supervised learning](https://github.com/lightly-ai/lightly)
64.  [Trax — Deep Learning with Clear Code and Speed](https://github.com/google/trax)
65.  [Flax - a neural network ecosystem for JAX that is designed for flexibility](https://github.com/google/flax)
66.  [QuickVision](https://github.com/Quick-AI/quickvision)
67.  [Colossal-AI - An Integrated Large-scale Model Training System with Efficient Parallelization Techniques](https://github.com/hpcaitech/ColossalAI)
68.  [haystack: an open-source neural search framework](https://haystack.deepset.ai/docs/intromd)
69.  [Maze](https://github.com/enlite-ai/maze) - Application-oriented deep reinforcement learning framework addressing real-world decision problems.
70.  [InsNet - A neural network library for building instance-dependent NLP models with padding-free dynamic batching](https://github.com/chncwang/InsNet)

### Tools

1.  [Nebullvm](https://github.com/nebuly-ai/nebullvm) - Easy-to-use library to boost deep learning inference leveraging multiple deep learning compilers.
2.  [Netron](https://github.com/lutzroeder/netron) - Visualizer for deep learning and machine learning models
2.  [Jupyter Notebook](http://jupyter.org) - Web-based notebook environment for interactive computing
3.  [TensorBoard](https://github.com/tensorflow/tensorboard) - TensorFlow's Visualization Toolkit
4.  [Visual Studio Tools for AI](https://www.microsoft.com/en-us/research/project/visual-studio-code-tools-ai/) - Develop, debug and deploy deep learning and AI solutions
5.  [TensorWatch](https://github.com/microsoft/tensorwatch) - Debugging and visualization for deep learning
6. [ML Workspace](https://github.com/ml-tooling/ml-workspace) - All-in-one web-based IDE for machine learning and data science.
7.  [dowel](https://github.com/rlworkgroup/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](https://neptune.ai/) - Lightweight tool for experiment tracking and results visualization. 
9.  [CatalyzeX](https://chrome.google.com/webstore/detail/code-finder-for-research/aikkeehnlfpamidigaffhfmgbkdeheil) - Browser extension ([Chrome](https://chrome.google.com/webstore/detail/code-finder-for-research/aikkeehnlfpamidigaffhfmgbkdeheil) and [Firefox](https://addons.mozilla.org/en-US/firefox/addon/code-finder-catalyzex/)) that automatically finds and links to code implementations for ML papers anywhere online: Google, Twitter, Arxiv, Scholar, etc.
10. [Determined](https://github.com/determined-ai/determined) - Deep learning training platform with integrated support for distributed training, hyperparameter tuning, smart GPU scheduling, experiment tracking, and a model registry.
11. [DAGsHub](https://dagshub.com/) - Community platform for Open Source ML – Manage experiments, data & models and create collaborative ML projects easily.
12. [hub](https://github.com/activeloopai/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.
13. [DVC](https://dvc.org/) - 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.
14. [CML](https://cml.dev/) - CML helps you bring your favorite DevOps tools to machine learning.
15. [MLEM](https://mlem.ai/) - 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](http://on-demand-gtc.gputechconf.com/gtcnew/on-demand-gtc.php?searchByKeyword=shelhamer&amp;searchItems=&amp;sessionTopic=&amp;sessionEvent=4&amp;sessionYear=2014&amp;sessionFormat=&amp;submit=&amp;select=+)
2.  [100 Best Github Resources in Github for DL](http://meta-guide.com/software-meta-guide/100-best-github-deep-learning/)
3.  [Word2Vec](https://code.google.com/p/word2vec/)
4.  [Caffe DockerFile](https://github.com/tleyden/docker/tree/master/caffe)
5.  [TorontoDeepLEarning convnet](https://github.com/TorontoDeepLearning/convnet)
6.  [gfx.js](https://github.com/clementfarabet/gfx.js)
7.  [Torch7 Cheat sheet](https://github.com/torch/torch7/wiki/Cheatsheet)
8. [Misc from MIT's 'Advanced Natural Language Processing' course](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-864-advanced-natural-language-processing-fall-2005/)
9. [Misc from MIT's 'Machine Learning' course](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/lecture-notes/)
10. [Misc from MIT's 'Networks for Learning: Regression and Classification' course](http://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-520-a-networks-for-learning-regression-and-classification-spring-2001/)
11. [Misc from MIT's 'Neural Coding and Perception of Sound' course](http://ocw.mit.edu/courses/health-sciences-and-technology/hst-723j-neural-coding-and-perception-of-sound-spring-2005/index.htm)
12. [Implementing a Distributed Deep Learning Network over Spark](http://www.datasciencecentral.com/profiles/blogs/implementing-a-distributed-deep-learning-network-over-spark)
13. [A chess AI that learns to play chess using deep learning.](https://github.com/erikbern/deep-pink)
14. [Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMind](https://github.com/kristjankorjus/Replicating-DeepMind)
15. [Wiki2Vec. Getting Word2vec vectors for entities and word from Wikipedia Dumps](https://github.com/idio/wiki2vec)
16. [The original code from the DeepMind article + tweaks](https://github.com/kuz/DeepMind-Atari-Deep-Q-Learner)
17. [Google deepdream - Neural Network art](https://github.com/google/deepdream)
18. [An efficient, batched LSTM.](https://gist.github.com/karpathy/587454dc0146a6ae21fc)
19. [A recurrent neural network designed to generate classical music.](https://github.com/hexahedria/biaxial-rnn-music-composition)
20. [Memory Networks Implementations - Facebook](https://github.com/facebook/MemNN)
21. [Face recognition with Google's FaceNet deep neural network.](https://github.com/cmusatyalab/openface)
22. [Basic digit recognition neural network](https://github.com/joeledenberg/DigitRecognition)
23. [Emotion Recognition API Demo - Microsoft](https://www.projectoxford.ai/demo/emotion#detection)
24. [Proof of concept for loading Caffe models in TensorFlow](https://github.com/ethereon/caffe-tensorflow)
25. [YOLO: Real-Time Object Detection](http://pjreddie.com/darknet/yolo/#webcam)
26. [YOLO: Practical Implementation using Python](https://www.analyticsvidhya.com/blog/2018/12/practical-guide-object-detection-yolo-framewor-python/)
27. [AlphaGo - A replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search"](https://github.com/Rochester-NRT/AlphaGo)
28. [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers)
29. [Machine Learning is Fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.oa4rzez3g)
30. [Siraj Raval's Deep Learning tutorials](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A)
31. [Dockerface](https://github.com/natanielruiz/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](https://github.com/ybayle/awesome-deep-learning-music) - Curated list of articles related to deep learning scientific research applied to music
33. [Awesome Graph Embedding](https://github.com/benedekrozemberczki/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](https://github.com/chihming/awesome-network-embedding) - Curated list of articles related to deep learning scientific research on graph structured data at the node level.
35. [Microsoft Recommenders](https://github.com/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](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) - Andrej Karpathy blog post about using RNN for generating text.
37. [Ladder Network](https://github.com/divamgupta/ladder_network_keras) - Keras Implementation of Ladder Network for Semi-Supervised Learning 
38. [toolbox: Curated list of ML libraries](https://github.com/amitness/toolbox)
39. [CNN Explainer](https://poloclub.github.io/cnn-explainer/)
40. [AI Expert Roadmap](https://github.com/AMAI-GmbH/AI-Expert-Roadmap) - Roadmap to becoming an Artificial Intelligence Expert
41. [Awesome Drug Interactions, Synergy, and Polypharmacy Prediction](https://github.com/AstraZeneca/awesome-polipharmacy-side-effect-prediction/)

-----
### Contributing
Have anything in mind that you think is awesome and would fit in this list? Feel free to send a [pull request](https://github.com/ashara12/awesome-deeplearning/pulls).

-----
## License

[![CC0](http://i.creativecommons.org/p/zero/1.0/88x31.png)](http://creativecommons.org/publicdomain/zero/1.0/)

To the extent possible under law, [Christos Christofidis](https://linkedin.com/in/Christofidis) has waived all copyright and related or neighboring rights to this work.
