I’ve been playing around with Flux since I’m trying to learn deep learning. Since I am an extreme beginner in the subject I’ve been trying to learn by reading as much as possible but the best understanding usually comes from playing around with example code. I really appreciate the examples/explanations in flux docs and would love to see more. My interest is really centered on learning more than anything.
NOTE: I’m particularly interested right now in learning how convolution layers/neurons are trained. Whereas, I do understand convolution in general since I’ve worked extensively on several projects based on those principals, it’s not yet clear to me how this takes place in the context of deep learning. Any help in this respect would be very much appreciated. I should probably mention that I do not have a strong background in math and generally understand much more quickly when I see code (or pseudo-code).