Last night I was looking into what to read after or along with 3Blue1Brown's series of Linear algebra videos [1]

The contenders seems to be:

- Linear Algebra Done Right - Sheldon Axler

- Liner Algebra Done Wrong - Sergei Treil

- Introduction to Linea Algebra - Gilbert Strang

- Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe

[1] https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2x...

Previously on Hacker News:

Linear Algebra Done Right 58 points, July 2023, 4 comments https://news.ycombinator.com/item?id=36576114

Linear Algebra Done Right – 4th Edition, 631 points, Oct 2023, 294 comments https://news.ycombinator.com/item?id=38060159

Linear Algebra Done Right [pdf], 85 points, Sept 2024, 39 comments https://news.ycombinator.com/item?id=41416799

Human verification failed - so this is a broken link.

I got halfway though the exercises with the help of a reading group. They were very hard, bit thought provoking, so I would definitely recommend. Don't feel discouraged if you get stuck and try not to look at the solutions right away.

For those who find Linear Algebra Done Right too much to start with, and those who don't get why Strang starts with matrices, I can't recommend more "The dark art of linear algebra" read this first. With this you can then tackle every other book on the topic more easily

Note that "done right" means done with Axler's completely subjective and unusual hatred of determinants, chronicled here [0]. It is in no way "done right" in some definitive, rigorous way; most math professors I have spoken to either strongly disagree with the presentation or have no particular preference.

[0] https://www.axler.net/DwD.html

This is supposedly based on Sheldon Axler's earlier and shorter paper "Down With Determinants!" [0]. I lectured mathematics for a while at a "former polytechnic" and used to enjoy leaving print-outs of this sort of paper in the faculty communal areas.

[0] https://www.axler.net/DwD.html

I found it really insightful (and always overlooked) to distinguish between vector and co-vector spaces. It doesn't necessarily produce new knowledge, but makes things more clear.

I wanted to learn the underlying principles of LLM/AI and got myself Shilov's book. Wow that was so thick, each paragraph took a while to figure. This could be a nice option..

Thanks!

Lately been deep diving into linear algebra. And a way which i engage with it is that I tell AI to generate interactive examples + questions on Lean or Haskell. Its so fun, just deriving the intuition in these languages.

i literally threw this book in the trash cause it was too dense and pretentious.

I passed the class just because of how good the book is.

This is a holy book for a lot of game developers.

Kindle format link == 404

Overrated and tendentious book. There are many better linear algebra texts. His polemic against determinants is poorly motivated, misguided, and distracting. The writing is quite formal and not terribly inspiring. The coverage is adequate but nothing more.

My bag of tricks is better than your bag of tricks. Alright.

As with most textbooks, it fails to motivate why reading it is worth the investment. Perhaps it is a millennial old tradition of the Greek mystery schools, that the rite of passage came by proving your commitment to material knowledge without anything but fate in the school itself as motivation.

Rigor before Worth.

(Yes this is a pet peeve of mine :)

These days, linear algebra done right should be accompanied with some CAS to view how algorithms are used.

Possibly paired with some numerical algebra free text (many on the Internet)

Do not get the latest edition, the layout and typesetting is atrocious!

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