Thanks, appreciate you like it. I tried to keep it simple and focused on the key insight, i.e., replacing dumb functions that make up an intelligent function with more intelligent functions that make up an even more intelligent function. ;)

That's great although I think using NN with small data and small training (e.g., on your computer) is likely to be overly complex. Their strength is mostly on HUGE data with MASSIVE compute due to the scaling laws.

@@finxter That's true. Real power is in big numbers. However, my experience shows that some significant estimations can be made even on simple networks. For me, the network has to vagely recognize the scene and asess image quality. I suppose that can be done even on moderately sized networks. As for training dataset, mine counts tens of thousands images. Dataset has only two categories: accepted and regected. And program has to guess likelihood that given image will be in one of those categories. On other words, NN gives a number between 0 and 1, but tries to be as close to a expected answer as possible. As I said, results are quite acceptable. One notable obstacle is that I have no Nvidia card, so that had to be set up to work on Radeon and under Windows. Still, it works and I'm content.

Fair. For instance check out this one for a more technical explanation (without memes as neurons): kzfaq.info/get/bejne/edFznM2Svdelf4E.htmlsi=90V0Vj11XlRhnKvD