This might be quite a broad question, but I was wondering if I could leverage the wisdom of the people on this site.
So I have recently started to learn about deep learning. I have written a simple feedforward network from scratch, learnt how to code up MLPs, and CNNs using various kinds of regularization techniques in Tensorflow.
I have started to read papers, and this was when it occurred to me that there is still so much to learn and I'm a bit overwhelmed by how much there is.
Furthermore, while studying RMBs I read that they are hardly used today for pre-training but rather batch normalization is used, so I don't want to be studying something that is already out of date!
As I am self-studying are there any suggestions as to what areas of deep learning that should take priority after MLPs, CNNs and RMBs? Or this question is too hard to answer, I guess the next best question is how would you study deep learning if you could do it again?