I work in deep learning, and I'm very dubious of the benefit of terse code. In my experience 90% of novel deep learning models can be coded with base layer clas

I work in deep learning, and I'm very dubious of the benefit of terse code. In my experience 90% of novel deep learning models can be coded with base layer classes from tf/pytorch/keras + maybe a few hundred lines of 'new' code. For this code I feel like you will avoid mistakes and make things so much easier for the reader (probably yourself in 6 months), if you modularise, write it as clearly as possible and explain what each part is doing. I have used code from the fast.ai codebase and personally I find it horrendous to work with - I find the way they structure and name classes consfusing, they use wildcard imports everywhere, they use tiny variable names for everything - it's all extremely reader-unfriendly, which for an educational tool seems completely bizarre.