An internal covariate shift occurs when there is a change in the input distribution to our network. When the input distribution changes, hidden layers try to learn to adapt to the new distribution. This slows down the training process. If a process slows down, it takes a long time to converge to a global minimum. This problem occurs when the statistical distribution of the input to the networks is drastically different from the input that it has seen before. Batch normalization and other normalization techniques can solve this problem. We will explore these in the following sections.