Batch Normalization: Why It Works

This lecture delves into the concept of batch normalization, a crucial technique in training deep neural networks. The instructor explains the aim of batch normalization, which is to keep the mean input stable around zero. The lecture covers the idea behind batch normalization, the normalization process on each input line, and the parameters involved in the batch normalizing transform. Additionally, the lecture discusses the training process to optimize parameters and the necessary role of batch normalization for ReLu and other unbalanced hidden units. The instructor also touches upon the importance of batch normalization in solving the vanishing gradient problem. Recommended readings include chapters from 'Deep Learning' by Goodfellow et al. and relevant papers on batch normalization.