Learning with Deep Neural Networks

This lecture delves into the reasons behind the success of deep learning, exploring how larger networks simplify the optimization problem, mitigate gradient issues, and benefit from vast labeled datasets. It also discusses the intriguing phenomenon of deep networks simultaneously overfitting and generalizing, challenging classical statistical learning theory. The lecture concludes by highlighting the empirical performance of deep learning in various domains and the ongoing challenges in semi-supervised learning.