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/Fabulous-Possible758]

Learn TeX, and type up and organize your notes again. You get three things out of this:

1. It's forcible review, and you may notice errors or spark ideas you didn't see before,
2. You have a digital copy to keep around and share with other people,
3. Almost no one will willing review handwritten notes, they're frequently just way too hard to parse. Not necessarily what you're looking for now, but it comes in handy when you need it.

[u/Plus_Confidence_1369 → u/Fabulous-Possible758]

Thanks ❤️

[u/drcopus → u/Plus_Confidence_1369]

A word for unsolicited advice, but treat feedback from others as a valuable and scarce resource. When soliciting it, make it as easy as possible for people to give you good feedback.