It's for a CS course at Stanford not a PyTorch boot camp. It seems reasonable to expect some level of academic rigour and need to learn and demonstrate understanding of the fundamentals. If researchers aren't learning the fundamentals in courses like these where are they learning them?

You've also missed the point of the article, if you're building novel model architectures you can't magic away the leakiness. You need to understand the back prop behaviours of the building blocks you use to achieve a good training run. Ignore these and what could be a good model architecture with some tweaks will either entirely fail to train or produce disappointing results.

Perhaps you're working at a level of bolting pre built models together or training existing architectures on new datasets but this course operates below that level to teach you how things actually work.