Author's): TANVEER MUSTAFA
Originally published in Towards Artificial Intelligence.
5 Normalization Techniques: Why Activation Standardization Changes Deep Learning
Training deep neural networks is difficult. Add more layers and the training becomes unstable – gradients explode or disappear, learning slows down, or the model fails to converge.
This article discusses five normalization techniques necessary to stabilize the training of deep learning models: batch normalization, layer normalization, instance normalization, ensemble normalization, and RMS normalization. Each method uniquely addresses internal covariate shift issues and illustrates how their implementation improves model performance on a variety of tasks, from computer vision to natural language processing, making deep networks more robust and efficient.
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Published via Towards AI
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