# Batch norm: why the initial normalization?

I'm a beginner in NNs and the first thing I don't understand with batch norm is the following 2 steps:

• First we normalize the batch data on a z parameter to Mu=0, sigma^2=1
• Then we change z via the coefficients of Mu, sigma^2 (usu. alpha, beta) by updating them as learnable parameters.

I don't understand why the first step is necessary if we change the distribution in the second step anyway. Could someone explain please?