How does batch normalization work?

Can you explain how batch normalization works in neural networks?

Sure! Batch normalization is a technique to improve the training of deep neural networks by normalizing the inputs of each layer.

Why is it important to normalize the inputs?

Normalizing inputs helps to stabilize the learning process, reduces the number of training epochs needed, and can lead to better performance.

How does it affect the learning rate?

Batch normalization allows for higher learning rates, which can speed up training and improve convergence.

Are there any downsides to using batch normalization?

Yes, it can add computational overhead and may not work well with certain types of models, like those with small batch sizes.