Valuable Resources for machine learning, deep learning, reinforcement learning, etc.
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KAIST 문일철 교수 강의
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David MacKay video lecture
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Carl Edward Rasmussen video lecture
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Michael Zibulevsky video lecture
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Autoencoder
- David MacKay, Information Theory, Inference, and Learning Algorithms: [link]
- David Barber, Bayesian Reasoning and Machine Learning: [link]
- Shai Shalev-Shwartz and Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms: [link]
- Carl Edward Rasmussen and Christopher K. I. Williams, Gaussian Processes for Machine Learning: [link]
- Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning: [link]
- Gaussian Processes
- Recurrent Neural Network (RNN)
- Reinforcement Learning: Basic concepts - Joelle Pineau
- Policy Search for RL - Pieter Abbeel
- Excellent short video clip about TRPO by Crazymuse AI
- David Silver's lecture
- UC Berkeley CS 294: Deep Reinforcement Learning
- Deep RL Bootcamp, Berkeley CA, 26-27 August 2017
- Pang-Yo Lab
- Columbia University IEOR 8100: Reinforcement Learning by Shipra Agrawal (not video, but useful)
- Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction: [link]
- Dimitri P. Bertsekas, REINFORCEMENT LEARNING AND OPTIMAL CONTROL: [link]
- Also many lecture videos and slides are available.
- Miguel Morales, Grokking Deep Reinforcement Learning: [link]
- Can see the entire book for free by clicking the table of contents
- OpenAI Spinning Up
- Policy Gradient
- TRPO
- Proximal Policy Optimization (PPO)
- Model-based Reinforcement Learning
- Multi-agent Reinforcement Learning
- How to build your own AlphaZero AI using Python and Keras
- Teaching a machine to master car racing and fireball avoidance through “World Models”
- Convex Optimization