Bagging: Regularization Method in Deep Learning

This lecture covers the concept of bagging as a regularization method in deep learning, where multiple variants of a model are trained on different subsets of data to improve generalization. The instructor explains the bagging algorithm, how each model can be a deep network, and how the models see different data sets. The lecture also includes a quiz on the benefits of averaging predictions over multiple models in machine learning competitions.