@DanKornas: Stop learning ML math from random tabs. Mathematics for Machine Learning is a curated GitHub collection of books, paper…
Summary
A curated GitHub collection (Mathematics for Machine Learning) that organizes books, papers, video lectures, and math basics for learning the math behind machine learning, covering linear algebra, calculus, probability, statistics, and more.
View Cached Full Text
Cached at: 05/26/26, 05:03 AM
Stop learning ML math from random tabs.
Mathematics for Machine Learning is a curated GitHub collection of books, papers, video lectures, and math basics for learning and reviewing the math behind machine learning.
It helps you build a stronger foundation by grouping reliable resources around the concepts ML engineers keep running into: linear algebra, calculus, probability, statistics, information theory, matrix calculus, and deep learning math.
Key features:
• Books first – points to Mathematics for Machine Learning, Deep Learning math basics, Probabilistic ML, Bayesian modeling, and deep learning math references • Papers included – links focused reads like matrix calculus for deep learning and an overview of mathematics in AI • Video lecture paths – includes multivariate calculus, linear algebra, and CS229 lecture playlists • Math basics section – collects statistics, probability, information theory, linear algebra, and calculus primers • Short notes per resource – each entry gives context so you can decide what to open next
Free public GitHub repo.
Link in the reply
Similar Articles
@antoniolupetti: "Matrix Calculus for Machine Learning and Beyond" is an interesting set of free lecture notes for understanding the mat…
A set of free MIT lecture notes on matrix calculus for machine learning, combining rigorous mathematics with visual explanations.
@Ellieorange8: Mathematics Master's Strongly Recommended Math Learning Resource: awesome-math The truly high-quality math textbooks, videos, and problem sets are all compiled in a GitHub list called Awesome Math, with 14k+ stars. It breaks down 30+ fields including algebra, geometry, analysis, probability, number theory into systematic...
Introduces a GitHub repository called Awesome Math that organizes free high-quality resources (videos, textbooks, problem sets) across 30+ math fields including algebra, geometry, analysis, etc. Updated continuously, suitable for math learners.
All fundamental knowledge in ML Course by Andrew NG that I noted and create into a repo github [R]
A user shares a GitHub repository containing detailed lecture notes for all 10 chapters of Andrew Ng's Machine Learning Specialization, written in LaTeX and automatically compiled to PDF.
@cyrilXBT: Andrew Ng just taught the entire mathematical foundation of machine learning in one lecture. Free. Stanford University …
Andrew Ng shares his Stanford CS229 lecture covering core machine learning mathematics, including locally weighted regression, maximum likelihood, logistic regression, and Newton's method, providing developers with a comprehensive guide to ML fundamentals.
stefan-jansen/machine-learning-for-trading
The article introduces the GitHub repository for the book 'Machine Learning for Trading' (2nd edition), which provides over 150 Jupyter notebooks covering ML techniques for algorithmic trading, including feature engineering, supervised/unsupervised learning, deep learning, and reinforcement learning.