The idea here is to standardize the data before sending it to another layer. This approach helps reduce the impact of previous layers by keeping the mean and variance constant. It also makes the layers independent of each other to achieve rapid convergence. For example, when we normalize features from 0 to 1 or from 1 to 100, it helps accelerate the learning cycle.