Batch Normalization

Batch Normalization is a crucial technique introduced to improve the speed, performance, and stability of training artificial neural networks, particularly deep networks. It addresses the problem known as "internal covariate shift," which describes the phenomenon where the distribution of activation values for each layer changes during training due to the continuous updating of parameters in the preceding layers. This shifting distribution can slow down the training process, make it sensitive to initial weights, and necessitate lower learning rates, hindering the network's ability to learn effectively.