Batch Normalization

Batch Normalization is a technique used in training deep neural networks to improve performance and speed up the learning process. It works by normalizing the input of each layer based on the mean and variance of the current batch of data. This helps to stabilize the learning process, reduces sensitivity to changes in weight distribution, and allows for higher learning rates. As a result, models train faster and often achieve better accuracy, making it a popular method in modern machine learning practices.