I may be reading too deeply into your question and you don't actually mean that theoretical. For something lighter, I'd suggest studying some of the common model architectures in the area you're interested in (like you can look at the pytorch lightning or torchvision or detectron2 models or etc source code) and understanding why things are the way they are.
[0] https://topml.rice.edu/ (this is an annual conference, can look at last years talks and references therein, search for topml, etc)
You will be very disappointed if you expect that there is some grand theory of Deep Learning.
My suggestion is to follow Arxiv Sanity: http://www.arxiv-sanity.com/top
Advances in DL have been mostly empirical not theoretical from what I have seen. It's less about being able to "envisage" and more about empirical testing.