Batch Normalization

Batch normalization is a technique used to stabilize and accelerate the training of deep neural networks by normalizing the inputs to each layer. It works by subtracting the batch mean and dividing by the batch standard deviation, which ensures that the activations have a consistent distribution throughout the training process.

This reduces the internal covariate shift, allowing for higher learning rates and less sensitivity to initialization. In financial applications, this is particularly beneficial for deep models that must process noisy and heterogeneous market data.

It acts as a form of regularization, often reducing the need for other techniques like dropout. By ensuring that the inputs to each layer are well-behaved, batch normalization makes the training process much more robust and efficient.

It is a standard component in modern deep learning architectures used for high-frequency trading and derivative pricing. The result is a more stable and reliable model that can adapt to changing market conditions.