AI Glossary
Normalisation (Layer)
What is Normalisation (Layer)?
A technique used within neural networks to stabilise and accelerate training by normalising the activations of each layer. Layer normalisation is a key component of the transformer architecture. Without normalisation, deep neural networks suffer from training instability — gradients either vanish or explode.
Example in practice
Engineers training a large language model without layer normalisation would observe training instability — loss values exploding or vanishing — making it practically impossible to train deep networks at scale.
Learn more
See Normalisation (Layer) applied in a professional context through this free course.
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