[hnrj95]

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

[touisteur]

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 :-)