Dropout: Tricks of the Trade

This lecture covers the concept of Dropout as a regularization method in deep neural networks. It explores how Dropout suppresses hidden units during training, uses the full network for validation and test, and enforces representation sharing in hidden neurons. Dropout is presented as both an approximate bagging technique and a tool for feature sharing, providing a simple yet effective regularization method.