how to go from mathematical problem to neural network (and back)?

I am a little confused on how, you can find online papers that describe complex Machine Learning formulas in a mathematical/probabilistic way, and, in the other hands, easy tutorials that teach you how to use frameworks to create neural networks, without mentioning the maths behind.

What is not clear is, what is the correlation between these two worlds? What are the "parameters" that make you understand i.e. how many layer to code, what kind of perceptrons to use, etc?

to make an example:

Let's take this formula, which in Wikipedia Italy is described as "the standard learning algorytm": And suppose that size of w and x is 4. , and g(x) and f(x) are, for examples, linear functions. What next? Where do I start coding a neural network that solves this problem?

It would seem more logical to me to code this "directly" without defining perceptrons, convolution, layers etc.

• Hi @Barsaas. Please include your main question in the title Jun 5 '21 at 5:11