Layer normalization is a technique that normalizes the inputs across all features within a single training example, rather than across the batch. It stabilizes and accelerates training by keeping activations at consistent scales, and is a key component of transformer architectures. Unlike batch normalization, it works identically regardless of batch size, making it ideal for the variable-length sequences processed by language models.