Batch normalization is a technique used in machine learning and deep learning to improve the training convergence of artificial neural networks.

To understand the concept, let's first define what a batch is. In machine learning, a batch is a subset of the training data that is used in each iteration of the optimization algorithm.

Batch normalization is the process of normalizing the inputs of each batch. It does this by subtracting the batch mean and dividing by the batch standard deviation. This normalization helps to reduce the internal covariance shift, which occurs when the distribution of the input data changes during training.

By normalizing the inputs, batch normalization helps to stabilize the training process, improve the convergence rate, and reduce overfitting. It also helps to increase the robustness of the model to variations in the input data.

One of the main benefits of batch normalization is that it allows for the use of higher learning rates in the optimization algorithm. This is because it reduces the likelihood of diverging from the optimal solution due to large weight updates.

Overall, batch normalization is a powerful technique that has become a standard component in many deep learning architectures. Its ability to improve the convergence rate and reduce overfitting has made it an essential tool for building accurate and robust models.