Layer normalization is a technique used in deep learning to improve the training of neural networks. It normalizes the inputs across the features for each training example, rather than across the batch as in batch normalization. This helps to stabilize the learning process and can lead to faster convergence. Layer normalization is particularly useful in recurrent neural networks and transformers, where batch sizes may vary or where the model architecture does not lend itself well to batch normalization. Common use cases include natural language processing tasks and other sequential data applications.
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AI FundamentalsDiscover the concept of language modeling in NLP, its characteristics, and common use cases.
AI Fundamentals