The maths are not actually that complicated. 99% of what pytorch is doing is matrix multiplication and addition with n-dimensional arrays (tensors). For backprop you need a very limited understanding of calculus. [1] Stuff like sigmoid/softmax is also straightforward and you can grok it from a 5 minute youtube video. [2] If something doesn't make sense, just ask ChatGPT to walk you through it.
There is a lot of other complicated math that you will need when reading papers because people writing AI papers like to overcomplicate a lot of things that can be represented in 5 lines of pytorch.
[1] https://towardsdatascience.com/understanding-backpropagation...