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Comment by melling | Hacker News Reader

Any thoughts on acquiring the skills needed to understand linear algebra so it’s possible to read Axler’s Linear Algebra Done Right

https://linear.axler.net/

… or Mathematics for Machine Learning

https://mml-book.github.io/

There are YouTube videos for both books:

Axler: https://youtube.com/playlist?list=PLGAnmvB9m7zOBVCZBUUmSinFV...

MML: https://youtube.com/playlist?list=PLiiljHvN6z1_o1ztXTKWPrShr...

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Perhaps not what you’re looking for but if you can get through Griffith’s Quantum Mechanics you likely can get through Axler. I found it helpful to draw examples from QM when self studying in Linear Algebra Done Right.

Huh, somehow I also learned LA best through Griffiths QM. I almost wish Griffiths would write a Linear Algebra textbook.

I’ve been watching the 18.06SC linear algebra course on MIT OpenCourseWare (taught by Gilbert Strang) and it’s really great - one of the best courses I’ve ever seen. I’ve just started the follow-up course that covers applications.

Isn't it so good?! The way he starts the class having you thinking you missed a reading or something, and then by the end of the lecture you have an aha moment. Feels like he crams an aha moment into each lesson too, so good.

axler’s text is phenomenal, but it’s probably not what you’re looking for if you want an “applied” view into computational techniques gleaned from linear algebra. the text centers on finite dimensional vector spaces, in the standard, mathy, axiomatic way—which is far more general than the prototypical numerical usage in standard programming problems in most swe jobs

I agree here. I tried to power through the book (which to be fair is really clear, didactic and still concise) and I was stumped because I'm more practical minded, and I'd be trying to look for examples in my domain (signal processing: filtering, beamforming, mle/map, compression, etc.) and I'd be stumped quite fast.

I appreciate any textbook with interesting real world examples and slow worked-through solutions. But maybe I'm a bit too lazy to do the work myself.

I still haven't grasped really what svd does, why it is different from eigenstuff (and... well... what eigenvalues/vectors are...) and the link between those and solving linear systems, and with the characteristic polynomial, and matrix inversion, and... I have intuitions, and I can mostly implement the stuff, but no clear understanding.

So... I suck at learning linear algebra :-)

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