The empirical success of deep learning drives much of the excitement about machine learning today. This success vastly outstrips our mathematical understanding. This lecture surveys progress in recent years toward developing a theory of deep learning. Works have started addressing issues such as speed of optimization, sample requirements for training, effect of architecture choices, and properties of deep generative models.
This is the second of a two-part lecture, the first of which is here.
This video is part of Harvard University‘s two-part Alfors lecture 2018.
