Generally I found out that the math is only as complex as the notation, once translated in laymanâs itâs generally dead simple.
Also a lot of formula arenât actually equation, they are description, ie you canât use them unless you reconstruct the term on your own For example stuff like F(x)D(x)G(x) is not useful unless you look for what they are intended to represent (here itâs the fresnel term, the distribution term and the geometric term)
For example the scariest equation you find on internet are integral and big sigma, which in practice you gonna do a for loop, with the difference is that integral is summing sample*delta, while big sigma is just summing the samples.
For example neural network look scary until you realize thatâs the same math than a supermarket ticket, which is trivial, a single receipts being equivalent to a perceptron when you look at the result and decide if itâs over budget or not 
The other scary thing is all the symbol dialects, that is each math has is niche notation that get close but sometimes diverge, for example in shader omega tend to mean hemisphere or sphere, theta and phi are angle (horizontal and vertical respectively). But one doofus will use the omega to denote weight, and you can only understand if you are familiar with the subject already.
Also pi and radian use to be that stuff that makes you feel mathy, until you realize that in practical term pi is just half a cycle, so 2pi is a circle, substituting 2pi by circle make anything WAY more clear, you donât even convert to degree anymore because radiant are just fraction of circles, half a circle is easier than 180° or pi.
I have read some damning thing, for example the mcGuire SSGI paper is full of unnecessary implicit jargony notation dialect, if I hadnât read hundreds of other paper on GI would had scare me away.
Itâs a lot of unnecessary obfuscation.