Explain 'Batch Normalization'.

Explain 'Batch Normalization'.

Approach: Describe normalizing the inputs of each layer for each mini-batch.

A technique to make neural networks faster and more stable by normalizing the layer inputs for each mini-batch. It subtracts the batch mean and divides by the batch standard deviation, allowing for higher learning rates.

Follow-up: Does it act as a regularizer?

Key concepts: internal covariate shift, scaling, mean/variance.