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Can someone recommend a good book on linear algebra for somebody that took it in college but needs a refresher plus some advanced linear algebra concepts for machine learning?



The texts that I hate least (LA and I have a long and rocky relationship):

- Coding the Matrix, Klein https://www.amazon.com/Coding-Matrix-Algebra-Applications-Co... This has a strong emphasis on LA's utility in CS, and includes concepts outside traditional LA that enrich the narrative.

- Intro to Linear Algebra, Strang https://www.amazon.com/Introduction-Linear-Algebra-Gilbert-S... Strang approaches LA from a practical less-theoretical angle, which makes it very sensible if you're an engineer but may not be as suitable if you're a mathematician.

- Linear Algebra, A Modern Intro, Poole https://www.amazon.com/Linear-Algebra-Introduction-Available... This is a solid text that has worked out most of its bugs over the editions.

- Linear Algebra and its Applications, Lay https://www.amazon.com/Linear-Algebra-Applications-Updated-C... Like Poole, this is also a solid and long running text.

The books by Klein and Strang also benefit from free videos of those courses that are available from Coursera/BrownU and MIT OCW. Klein's is also available on the Kindle.


Thanks for taking the time to write that out. I'll check them out.


I highly recommend these youtube videos by 3Blue1Brown https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2x....

Each short lecture had a 'Why didn't they tell me that in college!' kind of insight. Transformations, determinants, dot products, cross products everything made sense in a visual way.


Vector Calculus, Linear Algebra, and Differential Forms: A Unified Approach http://matrixeditions.com/5thUnifiedApproach.html Thick, but after this book you ll be well prepared to ML.




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