Normalization

Normalization transforms data or internal activations into a more consistent scale or distribution. In preprocessing, numerical features may be rescaled so a large-valued variable does not dominate distance calculations or optimization. Inside neural networks, techniques such as batch normalization and layer normalization stabilize intermediate values and can make training faster or more reliable. These methods operate differently and are not interchangeable: batch normalization depends on batch statistics, while layer normalization works across features within an example. The correct choice depends on architecture, batch size, inference conditions, and data meaning. Normalization parameters learned during training must also be handled consistently after deployment to prevent silent changes in model behavior.