Subject
6 entries
Math
Bookmarks
Neural Networks — 3Blue1Brown
3Blue1Brown's neural networks playlist is the canonical visual introduction to how neural networks and backpropagation work — four episodes, starting from scratch and building to the chain rule. The most-watched math explainer for deep learning fundamentals.
Mathematical Foundations of Reinforcement Learning
Mathematical Foundations of Reinforcement Learning by Shiyu Zhao is an open-access textbook covering RL theory rigorously — Bellman equations, value functions, policy gradient methods — with a mathematical depth missing from most applied RL courses. Free on GitHub.
Program of Thoughts Prompting: Disentangling Computation from Reasoning
Program of Thoughts separates reasoning from computation by having LLMs write executable Python programs rather than performing arithmetic inline, delegating number-crunching to an interpreter. It substantially outperforms chain-of-thought on numerical reasoning benchmarks by eliminating the arithmetic errors that plague prose reasoning chains.
How Diffusion Models Work: The Math from Scratch
AI Summer's mathematical walkthrough of how diffusion models work from scratch — covering the forward noising process, reverse denoising, DDPM training objective, and score matching. The most math-forward accessible introduction to the field.
Solving Quantitative Reasoning Problems with Language Models (Minerva)
Lewkowycz et al. at Google Research introduce Minerva, a language model pretrained on general text and further trained on technical content that achieves state-of-the-art on quantitative reasoning benchmarks without external tools. It correctly answers nearly a third of undergraduate-level science problems — an early proof that domain-specific pretraining unlocks STEM reasoning at scale.
Data Analysis, Statistics, and Probability Overview
Annenberg Learner's Data Analysis, Statistics, and Probability course — a free online course covering statistical reasoning, data representation, and probability for educators and learners. A foundational resource for building statistical intuition before the MOOC era.