There are places where things like eigenvectors / eigenvalues or svd come into play, but those are pretty rare and not part of modern architectures (tbh, I still don't really have a good intuition for them).
Honestly, where stuff gets the most confusing to me is when the authors of the newer generations of AI papers invent new terms for existing concepts, and then new terms for combining two of those concepts, then new terms for combining two of those combined concepts and removing one... etc.
Some of this redefinition is definitely useful, but it turns into word salad very quickly and I don't often feel like teaching myself a new glossary just to understand a paper I probably wont use the concepts in.
Being really good at math does let you figure out if two techniques are mathematically the same but that’s fairly rare (it happens though!)
This stuff is part of modern optimizers. You can often view a lot of optimizers as doing something similar to what is called mirror/'spectral descent.'
https://mathacademy.com/courses/mathematics-for-machine-lear...
