I'm beginning to feel like any kind of specific approximation via neural networks is a myth. That if you can't reduce output to simple categorical values that can be broadly interpreted between two points, that it doesn't work.

I have some questions and they don't seem to be getting answered about the design of the net. How many layers should I use ? How many neurons per layer ? How does this relate to the number of desired quantitive scalar outputs I'm looking to create, even if they are normalized, they can vary GREATLY and will if I'm approximating the out of several mathematical expressions. Based on this and the expected error ranges of these numbers and how many possible major digits could be produced within the domain of the variable inputs being introduced, how many neurons per layer ? What does having more layers do ? In pytorch there don't seem to be a lot of layer types per say, but there are a crap ton of activation functions, and should I just be using these at the tail end or should they actually be inserted between layers so the input of the next layer passes through another series of actiavtion functions ? what does this do to the range of output ?

do I need to be a mathematician to do this ?

remembered successes removed quantifiable scalars entirely from output, meaning that I could interpret successful results from ranges of decimal points.

but i've had no success with actual multi variable regression as of yet, even when those input variables are only 2 and on limited value ranges eg [0,100] and [0, 2pi]

and then there are training epochs to avoid overfitting, and reasonable expectation of batches till quality results will start to form.