Batch normalization

1. What does batch normalization do?
2. When should I use batch normalization?
3. Why batch normalization is used in CNN?

What does batch normalization do?

Batch normalization is a technique for training very deep neural networks that normalizes the contributions to a layer for every mini-batch. This has the impact of settling the learning process and drastically decreasing the number of training epochs required to train deep neural networks.

When should I use batch normalization?

When to use Batch Normalization? We can use Batch Normalization in Convolution Neural Networks, Recurrent Neural Networks, and Artificial Neural Networks. In practical coding, we add Batch Normalization after the activation function of the output layer or before the activation function of the input layer.

Why batch normalization is used in CNN?

Batch Norm is a normalization technique done between the layers of a Neural Network instead of in the raw data. It is done along mini-batches instead of the full data set. It serves to speed up training and use higher learning rates, making learning easier. the standard deviation of the neurons' output.