Five free math textbooks that cover the math behind ML, with exercises included
cneuralnetwork · x · 2026-11-09
A curated list of five completely free math textbooks for machine learning, each with exercises rather than just theory, mapped to the concepts they help you understand:
- Introduction to Applied Linear Algebra (Boyd & Vandenberghe, Stanford): embeddings & PCA
- Mathematics for Machine Learning (Deisenroth et al.): backpropagation
- Introduction to Probability for Data Science (Chan): uncertainty & Bayesian ML
- Convex Optimization (Boyd): gradient descent & SVMs
- Dive into Deep Learning: hands-on deep learning
Boyd's linear algebra book site also offers lecture slides, videos, and Python/Julia companions; it's the official textbook for courses at Stanford and UCLA.
Related event: Five Free Math Textbooks Covering the Core Mathematics of Machine Learning(2 posts)→
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