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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A curated list of awesome Deep Learning tutorials, projects and communities.
| Date | Stars |
|---|---|
| 2026-07-24 | 28656 |
| 2026-07-25 | 28660 |
| 2026-07-28 | 28660 |
| 2026-07-30 | 28660 |
| 2026-07-31 | 28686 |
| 2026-08-06 | 28714 |
Today
+28 stars today
This week
+54 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.19%/day
# Awesome Deep Learning [](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.ca
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Chaitanya Prakash Bapat · Meta · United Kingdom
119
Chris
32
Rian Adam · Universitas Islam Indonesia / University of Central Florida · Indonesia
11
Ayan Das · Huawei R&D UK · United Kingdom
7
Stjepan Jureković · Manning Publication · Croatia
7
Benedek Rozemberczki · @google · United Kingdom
6
Sawyer Charles · United States
4
Prashanth Mangipudi · India
4
Sankalp
4
Philippe Castonguay
3
Lutz Roeder · @microsoft
3
Krishna Kalyan · Nvidia · Germany
3
Guillaume Chevalier · Canada
3
Sina Torfi · Meta · United States
3
3
Zhenchuan Huang
3
Terry Taewoong Um · Cosmax · South Korea
3
Leo Isikdogan
3
Hugo López-Fernández · Universidade de Vigo
2
Victor Schmidt · @Entalpic · France
2
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5d5519e331684c1b, topic:awesome, topic:awesome-list, desc:curated list
matched fp:5d5519e331684c1b, topic:deep-learning, topic:neural-network