What Are The Best Books On Linear Algebra For Machine Learning Beginners?

2025-07-11 03:15:35 244

4 Answers

Owen
Owen
2025-07-13 15:46:17
For total beginners, 'Linear Algebra for Dummies' by Mary Jane Sterling is a gentle start. It explains basics like matrix multiplication before easing into ML applications. Pair it with free resources like Gilbert Strang’s MIT OpenCourseWare for deeper dives. Avoid advanced books initially; build intuition first.
Uma
Uma
2025-07-14 10:06:14
When I first tackled ML, I wish someone had told me: skip the dry textbooks. 'Immersive Linear Algebra' by J. Ström et al. lets you interact with 3D diagrams online—way more engaging than static pages. Another underrated pick is 'Practical Linear Algebra for Data Science' by Mike X Cohen; it cuts fluff and dives into SVD, PCA, and other ML-relevant topics fast.

If you’re time-crunched, focus on chapters about vector spaces and eigenvalues—they’re the backbone of algorithms like PCA. And don’t stress about proofs; ML uses applied linear algebra, not abstract theory.
Finn
Finn
2025-07-15 06:34:54
I understand the struggle of finding the right linear algebra book. 'Linear Algebra Done Right' by Sheldon Axler was a game-changer for me—it focuses on conceptual understanding rather than rote computation, which is perfect for ML beginners. Another gem is 'Mathematics for Machine Learning' by Marc Peter Deisenroth, which directly ties linear algebra to ML applications, making abstract concepts tangible.

For hands-on learners, 'No Bullshit Guide to Linear Algebra' by Ivan Savov breaks down complex topics with a no-nonsense approach. If you prefer a visual learning style, 'The Manga Guide to Linear Algebra' by Shin Takahashi is surprisingly effective, using storytelling to explain matrices and vectors. Lastly, Gilbert Strang’s 'Introduction to Linear Algebra' is a classic, though denser—best paired with his MIT lectures for clarity.
Gracie
Gracie
2025-07-16 19:27:17
I’ve spent years recommending books to students, and the best linear algebra book for ML beginners depends on your learning style. If you want rigor, 'Linear Algebra and Its Applications' by David Lay balances theory with practical examples. For a lighter touch, 'Coding the Matrix' by Philip Klein integrates programming exercises (in Python!) to reinforce concepts like matrix operations—super useful for ML.

Avoid overly theoretical texts early on; focus on books that connect dots to ML, like 'Deep Learning' by Ian Goodfellow (Chapters 2–4). Bonus tip: Pair any book with 3Blue1Brown’s 'Essence of Linear Algebra' videos for intuition. Trust me, visualizing transformations beats memorizing formulas any day.
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