The 5 Normalization Techniques: Why Standardizing Activations Transforms Deep Learning
Author(s): TANVEER MUSTAFA
Originally published on Towards AI.
The 5 Normalization Techniques: Why Standardizing Activations Transforms Deep Learning
Training deep neural networks is difficult. Add more layers, and training becomes unstable — gradients explode or vanish, learning slows, or the model fails to converge.
This article explores five normalization techniques essential for stabilizing the training of deep learning models: Batch Normalization, Layer Normalization, Instance Normalization, Group Normalization, and RMS Normalization. Each method uniquely addresses challenges posed by internal covariate shift and illustrates how their implementation enhances model performance across various tasks, ranging from computer vision to natural language processing, making deep networks more reliable and efficient.
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