Batch normalization is a technique used to improve the training speed and stability of neural networks. It works by normalizing the inputs to each layer, ensuring they have a mean of zero and a standard deviation of one. This helps prevent issues like exploding or vanishing gradients, especially in deep networks.
Batch normalization also reduces the sensitivity of the network to the initialization of weights and allows for higher learning rates, leading to faster convergence. The process includes normalizing the activations and then scaling and shifting them using learnable parameters.
It is widely used in modern neural network architectures, particularly in convolutional networks, and has become a standard practice for training deep learning models.