In logistic regression, normalizing INPUT FEATURES speeds up learning.
In deep learning, for any hidden layer, can we normalize the activation functions so as to train W, b faster?
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There are some debates whether you should apply normalizing before or after activation function.
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But in practice it is known that normalizing the values before activation is better.
- You let the model learn gamma and beta to reshape the distribution of Z.
- Note that you don't want your values in hidden layers to have a mean of 0 and variance of 1 since you want the advantage of non-linearity.
- Batch Norm is carried out between z and a.
※ This beta is different from the beta of momentum.
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