The goal is to standardize the data before passing it forward to the next layer. By keeping the mean and variance constant, this method helps to decrease the influence of preceding layers. It also makes the layers self-contained, allowing for fast convergence. When we normalize characteristics from 0 to 1 or 1 to 100, for example, the learning cycle is sped up.