The problem with deep learning is opposite. You can understand most of it with just high school math. Advanced math is mostly useless because of the dimensionality of neural nets.
> Advanced math is mostly useless because of the dimensionality of neural nets.
It depends what you mean by advanced math. There is a lot of math that only really comes into play because of the high dimensionality! For example math related to tensor wrangling, low rank approximations, spectral theory, harmonic theory, matrix calculus derivatives, universality principles, and other concepts that could be interesting or bewildering or horrifying depending how you react to it. Of course some of it is only linear algebra of the 'just high school math' kind but that's not how I would normally describe it. If you look at the math in the proofs in the appendices of the more technical AI papers on arxiv there is often some weird stuff in there, not just matrix multiply and softmax.
Well I suppose that in some sense you are right. You can do deep learning without even knowing any math at all, by plugging together libraries and frameworks that other people wrote.
Also maybe you will say that "higher" math is by definition a curiosity and if it's practical then it's not "higher".
But if those aren't your arguments, then you can consider one example that the tensor 'differentiable programming' libraries used in deep learning use automatic differentiation and matrix calculus. Matrices are taught in high school, and calculus is taught in high school, but matrix calculus generally isn't as far as I know. Or at least not at my high school. https://en.wikipedia.org/wiki/Matrix_calculus
That's like saying you understand state-of-the-art CFD code because you can read Fortran.
There are many aspects to learning systems that we still don't have any kind of grasp on, and will take more than a little advanced math (statistics/probability theory, transport theory, topology, etc.) to understand as a community.
Dunning-Kruger is probably more common in spaces like this one, where people carry social capital for being able to "spin up quickly". But the true meta-skill of upskilling is turning unknown unknowns (UU) into known unknowns (KU), and then into known knowns (KK). It's not enough to just jump from UU to KK through osmosis by reading blog posts on a news aggregator, because there will still be a huge space of unknowns not covered by that approach.
