[D] Learning Theory

I remember taking a class in college about statistical learning theory. We talked about VC dimension and derived some bounds on training examples vs. accuracy. I remember for neural networks specifically the bound was too relaxed to be practically useful.

Is this still the case? I'm curious, especially in the context of transformers.

accuracy case college context examples machinelearning networks neural networks statistical theory training transformers