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dair-ai/Mathematics for Machine Learning
Mathematics for Machine Learning is a curated learning repository that connects the core mathematics behind modern machine learning: linear algebra, calculus, probability, statistics and optimisation. It is a useful roadmap for learners who want concepts, references and practical study order rather than isolated formulas.
Overview
Mathematics for Machine Learning is a curated learning repository that connects the core mathematics behind modern machine learning: linear algebra, calculus, probability, statistics and optimisation. It is a useful roadmap for learners who want concepts, references and practical study order rather than isolated formulas.
What you can do
- Organise a learning path across the mathematical foundations of ML.
- Find books, courses, notes and exercises for each subject.
- Use it to identify gaps before moving to deep learning or research papers.
How to use this repository
- Start with the official guide and a small example.
- Adapt one example to a real mathematical problem or teaching activity.
- Use the GitHub issues and releases to keep up with the project.