DL's optimization is pretty simple SGD. But I would suggest that the type of optimization in deep learning is actually a lot more like geometry than calculus. This was more of a hunch until recently, but some results such as the successful application of tropical geometry and less exotic Riemannian geometry to the analysis of DL loss surfaces are showing some evidence for that hypothesis.