Deep Networks versus Shallow Networks: Artificial Neural Networks and Deep Learning

This lecture explores the comparison between deep networks and shallow networks in the context of artificial neural networks and deep learning. It delves into the reasons why deep networks perform better on real-world problems, touching on topics such as loss landscape, optimization methods, momentum, RMSprop, ADAM, and the No Free Lunch Theorem. The lecture also discusses the concept of distributed representation, the number of regions carved by hyperplanes in different input spaces, and the performance of deep networks in addressing classification tasks.