What is batch normalization

Batch Normalization is a technique used in Machine Learning to improve the performance of neural networks by normalizing the input layer by adjusting and scaling the activations. It is a type of regularization technique that helps to reduce the internal covariate shift and make the optimization process more stable.

Batch Normalization works by normalizing the input layer by adjusting and scaling the activations. This helps to reduce the internal covariate shift and make the optimization process more stable. Batch Normalization also helps to reduce overfitting by introducing noise to the input layer.

The way Batch Normalization works is by normalizing the input layer by adjusting and scaling the activations. The activations are adjusted by subtracting the batch mean and dividing by the batch standard deviation. This helps to reduce the internal covariate shift and makes the optimization process more stable.

Batch Normalization also helps to improve the speed of convergence. By normalizing the input layer, the gradients are more likely to move in the same direction, which helps to speed up the optimization process.

Batch Normalization also helps to reduce overfitting. By introducing noise to the input layer, it helps to reduce the effect of overfitting.

Overall, Batch Normalization is a powerful technique used in Machine Learning to improve the performance of neural networks. It helps to reduce the internal covariate shift, speed up the optimization process, and reduce overfitting.