What is batch normalization?

Answer

Batch Normalization (BatchNorm) normalizes the activations of each layer to have zero mean and unit variance, computed over the mini-batch, then scales and shifts with learnable parameters γ and β. Benefits: accelerates training by allowing higher learning rates, reduces sensitivity to weight initialization, acts as a regularizer (reduces need for dropout), and mitigates the internal covariate shift problem (distribution of layer inputs changing during training). It is placed after the linear transformation and before the activation function in practice.