In neural networks, ______________ normalization is applied to stabilize and speed up the training process.

In neural networks, batch normalization is applied to stabilize and speed up the training process.

Batch normalization helps to address the internal covariate shift by normalizing the inputs to each layer of the network, which allows for higher learning rates and improves convergence. By normalizing the output of a previous layer by subtracting the batch mean and dividing by the batch standard deviation, it ensures that the data fed into the next layer is well-scaled, leading to improved training efficiency and performance. Additionally, batch normalization can also act as a form of regularization, reducing the need for other regularization techniques like dropout.