What is Batch Normalization?
Batch Normalization (BN) is a technique used to improve the training of deep neural networks in the context of Natural Language Processing (NLP) and other applications. It addresses the problem of internal covariate shift, which refers to the changes in the distribution of network activations due to the updates in the model parameters during training.
By normalizing the inputs of each layer, BN helps stabilize and accelerate the training process. The main steps of batch normalization involve calculating the mean and variance of the output from the previous layer across a mini-batch of data. These statistics are then used to scale and shift the activations to ensure they maintain a standard distribution, which enhances the model's convergence during training.
Specifically for NLP, where models such as recurrent neural networks (RNNs) and transformers are frequently employed, batch normalization can significantly enhance performance by allowing deeper architectures and enabling faster training times. Moreover, it can reduce the dependency on careful initialization and the learning rate, simplifying tuning and leading to more robust models.
Despite its benefits, applying batch normalization has some challenges in sequential data processing. In such cases, alternative normalization techniques like Layer Normalization are often used. Overall, batch normalization remains a foundational technique in modern deep learning practices within NLP and AI.