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.

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!

Thank you for sharing the paper!

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