I understand how Neural Networks work and have studied its theory well. My question is at the intricacies of Deep Neural networks and perhaps is a bit beyond common understanding (as I have been told (or misled) from discussions). My question is: On the whole, is there a clear understanding of how mutation occurs within a neural network from the input layer to the output layer, for both supervised and unsupervised cases? Any neural network is a set of neurons + the connection weights. With each successive layer, there is a change in the input. Say I have a Neural Network that does movie recommendation on n parameters. Say if X is a parameter that stands for the movie rating on IMDB. In each successive stage, there is a mutation of input X to X' and further X'' and so on. While of course, we know how to mathematically talk about X' and X'', do we at all have a conceptual understanding as to what this variable is in its corresponding neural n-dimension? The neural weights which to the human eye might be set of random numbers but may mean something profound if we could ever understand what the neural weights 'represent'.
What is the nature of neural weights such that despite decades worth of research and use, there is no clear understanding of what these connection weights represent? Or rather, why has there been so little effort in understanding the nature of neural weights, in a non-mathematical sense given the huge impetus in going beyond the black box notion of AI.