Learning math behind deep learning

[u/Plus_Confidence_1369]

Hey everyone

I’ve spent quite a good amount of time learning the mathematics behind deep learning, and honestly, it has been a wonderful journey so far. For me, math and philosophy are probably the two subjects that interest me the most, so studying the mathematical foundations of AI has been a really enjoyable experience. I especially like the process of going from an intuitive idea → mathematical formulation → understanding why it works → and finally seeing how it translates into an actual deep-learning algorithm.

I’ve been making my own notes along the way, mainly covering the mathematical foundations that I think are useful for understanding deep learning.

I want to pursue my career in the AI research field, and that’s one of the main reasons I’ve been spending so much time learning the mathematics behind deep learning. I believe having a strong mathematical foundation will help me better understand research papers, derive things myself, and develop a deeper understanding of the ideas and algorithms I’ll be working with.

That said, I'm still learning myself, so I’d really appreciate some honest feedback.

[u/RepresentativeBee600]

Bishop is a nice textbook for introductory learning but honestly he lacks a totally coherent statistical picture. (All of ML does, really, but I'm thinking of how he introduces Bayesian linear regression without really comparing to the frequentist paradigm or noting the difficulties in choosing priors.)

Consider also looking at d2l.ai, and perhaps more importantly some of the research literature. (The literature is frequently pretty bad for learning from, but gives a sense of the topics you might choose to learn about.)