What Are The Best Linear Algebra Books For Machine Learning?

2025-07-13 09:50:25 120

3 Answers

Zoe
Zoe
2025-07-14 00:52:23
I've been diving into machine learning for a while now, and linear algebra is the backbone of it all. My absolute favorite is 'Linear Algebra Done Right' by Sheldon Axler. It's super clean and focuses on conceptual understanding rather than just computations, which is perfect for ML applications. Another gem is 'Mathematics for Machine Learning' by Deisenroth, Faisal, and Ong. It ties linear algebra directly to ML concepts, making it super practical. For those who want a classic, 'Introduction to Linear Algebra' by Gilbert Strang is a must—it’s thorough and has great intuition-building exercises. These books helped me grasp eigenvectors, SVD, and matrix decompositions, which are everywhere in ML.
Leah
Leah
2025-07-14 00:06:05
When I first started learning machine learning, I realized how crucial linear algebra was, but textbooks often felt too abstract. That changed when I found 'Linear Algebra and Its Applications' by David Lay. It’s accessible and has tons of real-world examples, which made matrices and transformations click for me. For deeper theory, 'Matrix Computations' by Golub and Van Loan is legendary—it’s dense but invaluable for understanding algorithms like PCA.

Another underrated pick is 'No Bullshit Guide to Linear Algebra' by Ivan Savov. It’s concise and skips the fluff, perfect for impatient learners like me. If you’re into visual learning, 'Coding the Matrix' by Philip Klein combines programming (Python) with linear algebra, which is a game-changer for implementing ML from scratch. Each of these books offers something unique, whether it’s rigor, practicality, or hands-on coding.
Tyler
Tyler
2025-07-17 17:15:48
As someone who struggled with math early on, I needed linear algebra books that didn’t assume I was a genius. 'Practical Linear Algebra for Data Science' by Mike X Cohen was a lifesaver—it explains things like matrix operations and eigenvalues in plain English. For a balance of theory and ML applications, 'Deep Learning' by Goodfellow, Bengio, and Courville has a brilliant linear algebra chapter. It connects dots like tensor operations to neural networks.

I also love 'The Manga Guide to Linear Algebra' by Shin Takahashi. Yes, it’s a manga, but it sneaks in serious math with humor and visuals. Finally, 'Linear Algebra: Step by Step' by Kuldeep Singh breaks down proofs gently. These books turned my fear of matrices into confidence, especially when tackling ML papers.
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