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In machine learning, normalization is a statistical technique with various applications. There are mainly two forms of normalization, data normalization and activation normalization. Data normalization, or feature scaling, is a general technique in statistics, and it includes methods that rescale input data so that they have well-behaved range, mean, variance, and other statistical properties. Activation normalization is specific to deep learning, and it includes methods that rescale the activation of hidden neurons inside a neural network.

Normalization is often used for faster training convergence, less sensitivity to variations in input data, less overfitting, and better generalization to unseen data. They are often theoretically justified as reducing covariance shift, smoother optimization landscapes, increasing regularization, though they are mainly justified by empirical success.^[1]

^ Huang, Lei (2022). Normalization Techniques in Deep Learning. Synthesis Lectures on Computer Vision. Cham: Springer International Publishing. doi:10.1007/978-3-031-14595-7. ISBN 978-3-031-14594-0.

Normalization (machine learning)