[r-bryan]

Check out this 156-page tome: https://arxiv.org/abs/2104.13478: "Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges"

The intro says that it "...serves a dual purpose: on one hand, it provides a common mathematical framework to study the most successful neural network architectures, such as CNNs, RNNs, GNNs, and Transformers. On the other hand, it gives a constructive procedure to incorporate prior physical knowledge into neural architectures and provide principled way to build future architectures yet to be invented."

Working all the way through that, besides relearning a lot of my undergrad EE math (some time in the previous century), I learned a whole new bunch of differential geometry that will help next time I open a General Relativity book for fun.

[minhaz23]

I have very little formal education in advanced maths, but I’m highly motivated to learn the math needed to understand AI. Should i take a stab at parsing through and trying to understand this paper (maybe even using AI to help, heh) or would that be counter-productive from the get-go and I'm better off spending my time following some structured courses in pre-requisite maths before trying to understand these research papers?

Thank you for sharing this paper!

[mixmastamyk]

You might take a course on linear-algebra, at K.A. for example:

https://www.khanacademy.org/math/linear-algebra

And any prereqs you need. I also find the math-is-fun site to be excellent when I need to brush up on something from long ago and want a concise explanation. i.e. A 10 minute review, more than a few pithy sentences, yet less than a dozen-hour diatribe.

https://www.mathsisfun.com/

[minhaz23]

Thank you for sharing this!

[Quizzical4230]

Thank you for sharing the paper!

The link is broken though and you may want to remove the `:` at the end.