Advanced R Programming
Layer normalization is a technique used in deep learning to stabilize and accelerate the training of neural networks by normalizing the inputs across the features of each layer. Unlike batch normalization, which normalizes across the batch dimension, layer normalization operates on individual training examples, ensuring that the mean and variance are computed across the features of a single sample. This makes it particularly useful for recurrent neural networks and other architectures where batch sizes may vary or where dependencies on previous computations exist.
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