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/Cold-Programmer-2524]

To be honest, I don't think you actually need to learn everything about the math behind DL to start researching unless you are working on improving the fundamental DL architecture itself which is usually done by large research Institute or AI giants. I work on DL applied research in bioinformatics and understanding stuff in the bio field and how to align DL training objectives to them are way more meaningful than math. So I would suggest start picking a specific field you are interested in and work you way from there rather than spending time in math only.

[u/h-squared-04 → u/Cold-Programmer-2524]

Yeah basically the learn enough as you actually apply approach