4 free math books that actually matter for ML: linear algebra to information theory
cneuralnetwork · x · 2026-11-10
cneuralnetwork shares four free books that genuinely helped them build ML math foundations:
- Linear algebra: Mathematics for Machine Learning (Deisenroth et al.) for attention, embeddings and SVD
- Probability: Introduction to Probability (Blitzstein & Hwang) for sampling, distributions and uncertainty
- Optimization: Convex Optimization (Boyd & Vandenberghe) for gradients, convexity and convergence
- Information theory: an information theory textbook
Links to all four books are included — a curated, topic-matched reading list for ML math.
Related event: Four Free Math Books to Build Your Machine Learning Foundations(2 posts)→
More from Research
- WorldFoundry: open-source unified infra for evaluating world models — OpenEnvisionLab · 2026-11-10
- Flide: exact state forking speeds decision-model agents up to 9.5x — NationalUniversityofSingapore · 2026-11-10
- JoVox: joint flow matching yields 8x training throughput, 1/15 FLOPs — KAUST · 2026-11-10
- Goodfire's Mechanistic Shaping Lifts Worst-Group Accuracy From 51.6% to 97.7% — burny_tech · 2026-11-10
- Dyna Robotics shows RL vs IK head-to-head on wheeled robot, unveils workflow agent Dyna-2.1 — chris_j_paxton · 2026-11-10
- Quanta: Injecting 'thoughts' into LLMs to test whether AI can introspect — burny_tech · 2026-11-10