# Normalization - Deep Learning

I understand how to normalize a matrix/vector so as to get it to smaller scale and speed up further tasks. I am curious to know why the zero-mean matrix is divided by the variance and how it helps. Edit: I am unable to understand the intuition behind dividing the matrix by the variance