batch normalization

Normalizing the input or output of the activation functions in a hidden layer. Batch normalization can provide the following benefits: - Make neural networks more stable by protecting against outlier weights. - Enable higher learning rates, which can speed training. - Reduce overfitting.

Practitioners refer to batch normalization when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.