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Introducing the open-source textbook 'Mathematical Foundations of Reinforcement Learning', which explains reinforcement learning in a simple yet mathematically rigorous manner. It comes with extensive videos and code implementations, suitable for learners with a basic background in probability theory and linear algebra.
This thread discusses the concept of 'Jagged Intelligence' in AI, framing it as a consequence of AI learning being an ill-posed inverse problem, and argues that external stabilizers like scaffolding and verification are essential.
This thread explains the intuition behind the Jacobian Matrix and its widespread applications in AI and machine learning, including backpropagation, normalizing flows, computer vision, and robotics.