Besides, I've never met a physicist or a (real) engineer who would go to such lengths to oversimplify the math part, even if it was only “tangential” to them.
Maybe I should’ve been more clear: what I meant is that these programmers-oriented resources are all about applications…maybe a bit too much. For instance, In this book I cannot find the algebraic structure of vector spaces, theorems about how certain linear operators such as the kernel or the image led to subspaces (which is a very important result) or a proper introduction to the spectral theorem, for both Euclidean and Hermitian spaces (which also allows you to introduce some nice functional analysis).
Why are programmers always so attracted by these interactive/over-simplified/lightweight versions of linear algebra? They all focus on the visual aspects while ignoring the real stuff (theorems, proofs, etc.).
> I don’t think a good learning resource gets worse[...]
Probably not, but they become irrelevant. The other day I found an old programming book at my parents’ and while it was still a terrific resource, I couldn’t image anyone learning a language from a book nowadays.
AI is doing the same thing but 100 times effectively than anything else.
Blog tutorials, guides, programming books and youtube tutorials. They are completely irrelevant in a time where you have a personal tutor willing to explain every single detail of a subject.
What’s the appeal of an editor like Emacs in 2026? Why’d anyone still use when most jobs nowadays require you to work inside a container (and therefore use VSCode container extension)?
What I don’t understand is why people dismiss this kind of progress with false claims. Especially when discussing programming, people start to act irrational using arguments from back in 2022.
I think that you can easily address your concerns about this new technology (since we all are concerned about the future) but at the same time acknowledge how revolutionary it is.
It’s basically up to the domain experts. What I found interesting in mathematical optimization/combinatorics (my fields of interest) when an AI proved some major results some time ago was probably dismissed as a boring fact by someone else. What OP is mentioning is just their personal preference and doesn’t reflect the actual opinion of the mathematical world.
I know this book is not intended to be used as a first reading on Linear Algebra but, to me, this text isn’t good even for a second(or a third) read. The author hurries up too much on certain parts. I think that Serge Lang’s Linear Algebra does a better job in explaining pretty much every topic of the subject.
Tried it for the first time 15 years ago and never looked back since then. Coming from Slackware, I remember feeling relieved due to the automatic dependency management.
I just wonder how they will name the release versions when they will run out of Toy Story characters.
Besides, I've never met a physicist or a (real) engineer who would go to such lengths to oversimplify the math part, even if it was only “tangential” to them.