Sure, but currently deep learning is more like experimental physics. You try stuff and see what works and empirically improve your understanding. Then you can generalize some heuristics from this and use that "recipe" in the future. You figured out ReLU suddenly made something work, yet Swish turned out better so you can forget about ReLU now. And as you can treat deep learning (in supervised mode) as non-linear optimization, I doubt we'll come up with a proper theory unless P=NP. We can't even understand far simpler non-linear optimization problems, not to mention ones which can be arbitrarily parametrized in 2nd order...