- https://mlcourse.ai
- https://www.fast.ai/
- Coursera:
- MIT:
- Stanford:
- http://cs229.stanford.edu/
- http://cs230.stanford.edu/syllabus.html
- http://web.stanford.edu/class/cs224d/syllabus.html
- http://web.stanford.edu/class/cs224n/syllabus.html
- YouTube https://youtu.be/OQQ-W_63UgQ
- http://cs231n.stanford.edu/
- https://web.stanford.edu/class/cs20si/syllabus.html
- http://web.stanford.edu/class/cs234/schedule.html
- http://cs230.stanford.edu/index.html
- https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about
- https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.md
- An Introduction to Statistical Learning: http://www-bcf.usc.edu/~gareth/ISL/
- http://students.brown.edu/seeing-theory/
- http://neuralnetworksanddeeplearning.com/
- https://www.deeplearningbook.org/
- https://mml-book.com
- "Mathematics for Machine Learning": http://gwthomas.github.io/docs/math4ml.pdf
- The Hundred-Page Machine Learning Book: http://themlbook.com/wiki/doku.php
- Roadmap: https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap
- https://distill.pub/
- https://paperswithcode.com/sota
- http://www.cs.cmu.edu/~aharley/
- https://docs.google.com/presentation/d/1kSuQyW5DTnkVaZEjGYCkfOxvzCqGEFzWBy4e9Uedd9k/preview?slide=id.g168a3288f7_0_58
- A brief history of neural nets and deep learning: http://www.andreykurenkov.com/writing/ai/a-brief-history-of-neural-nets-and-deep-learning/
- CNN visualizator: http://scs.ryerson.ca/~aharley/vis/
- https://www.kaggle.com/jack89roberts/the-journey-of-an-image-through-a-neural-network/notebook
- Tensorflow and deep learning without a PhD: https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist/#0
- Cheatsheet: https://ml-cheatsheet.readthedocs.io/en/latest/index.html
- CNN Visualizator: http://yosinski.com/deepvis#toolbox
- Stanford CS234: http://web.stanford.edu/class/cs234/schedule.html
- Berkeley CS188: "Intro to AI" http://ai.berkeley.edu/course_schedule.html
- Berkeley CS294: "Deep Reinforcement Learning" http://rail.eecs.berkeley.edu/deeprlcourse/
- https://simoninithomas.github.io/Deep_reinforcement_learning_Course/
- David Silver OpenAI blog: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html *
- https://spinningup.openai.com/en/latest/
- Deepmind classes: https://youtu.be/2pWv7GOvuf0 *
- Deepmind Course: https://www.youtube.com/watch?v=iOh7QUZGyiU&list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs
- History of reinforcement learning: https://youtu.be/ul6B2oFPNDM
- Deep reinforcement learning (4 lectures): https://youtu.be/aUrX-rP_ss4
- An introduction to Reinforcement Learning: https://youtu.be/JgvyzIkgxF0
- Deep RL Bootcamp 2017: https://youtu.be/qaMdN6LS9rA
- https://www.lpalmieri.com/posts/rl-introduction-00/
- https://joshgreaves.com/reinforcement-learning/introduction-to-reinforcement-learning/
- https://lilianweng.github.io/lil-log/2018/02/19/a-long-peek-into-reinforcement-learning.html
- Lessons Learned Reproducing a Deep Reinforcement Learning Paper: http://amid.fish/reproducing-deep-rl
- https://github.com/yandexdataschool/Practical_RL
- https://github.com/qfettes/DeepRL-Tutorials
- https://github.com/higgsfield/RL-Adventure-2
- https://github.com/dennybritz/reinforcement-learning
- https://github.com/keras-rl/keras-rl
- https://github.com/carpedm20/deep-rl-tensorflow
- https://github.com/kengz/SLM-Lab --> https://colab.research.google.com/drive/1GmB_SqtmYj0YN6SKD9wn-D5LB2qBj2Ok#scrollTo=eNLMWPbcHJql
- Andrej Karpathy’s Pong from Pixels: http://karpathy.github.io/2016/05/31/rl/