I would say deep learning is a type of regression. The architecture of the network dictates the form of the equation. We're betting that equations of that form: 1) can fit the data 2) can generalize well to data that it wasn't fit against and 3) fitting/training is tractable. That allows making predictions about complicated data like pictures, words, sounds, etc. We may know the picture and need to predict a label for it.