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 should we use batch normalization?

Batch normalization solves a major problem called internal covariate shift. It helps by making the data flowing between intermediate layers of the neural network look, this means you can use a higher learning rate. It has a regularizing effect which means you can often remove dropout.

Why batch normalization is used in CNN?

Batch normalization is a layer that allows every layer of the network to do learning more independently. It is used to normalize the output of the previous layers. ... Using batch normalization learning becomes efficient also it can be used as regularization to avoid overfitting of the model.