🤯 Did You Know (click to read)
Layer normalization differs from batch normalization by computing statistics per example rather than across a batch, making it ideal for sequence models.
Layer normalization normalizes the inputs across the feature dimension for each training example, reducing internal covariate shift. In Transformer blocks, layer normalization is applied before or after attention and feed-forward sublayers. This improves gradient flow, enables deeper stacking of layers, and stabilizes training on large datasets.
💥 Impact (click to read)
Layer normalization ensures consistent training dynamics, allowing models to scale in depth and handle billions of parameters effectively.
For AI engineers, understanding layer normalization is crucial for designing stable architectures and troubleshooting training instabilities in large Transformer models.
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