Batch Normalization

What is Batch Normalization?

Batch normalization is a method used in the training process of deep learning models to normalize the input of each layer. This technique helps to mitigate the problem of internal covariate shift, where the distribution of inputs to a learning system changes over time, making training difficult and slow. By normalizing the inputs of each layer, batch normalization allows the network to learn more efficiently and helps in reducing the number of training epochs needed. Additionally, it can act as a form of regularization, reducing the need for other regularization techniques like dropout. This leads to more stable and faster training, as well as improved performance of the neural network.

A technique used in training deep neural networks to improve training speed and stability.

Examples

• Image Classification: In image classification tasks, batch normalization has been used to improve the performance of convolutional neural networks (CNNs). For instance, in the famous ResNet architecture, batch normalization is applied after each convolutional layer, enabling the network to train faster and achieve higher accuracy.


• Natural Language Processing (NLP): In NLP applications such as language translation, batch normalization can be used within recurrent neural networks (RNNs) or transformers. This helps the model to better understand and generate human languages by stabilizing the training process and allowing for deeper architectures.

Additional Information

• Batch normalization can be applied before or after the activation function of a layer.


• It introduces two additional parameters per layer, scaling and shifting, which are learned during training.