Layer Normalization: Faster Training and Steady Neural States
Neural networks learn faster when the signals inside them are kept calm.
A method called layer normalization does this by adjusting values across a whole layer for each single example, so training becomes smoother and quicker.
It remove the need to rely on groups of examples, which means it works the same way during practice and real use.
That makes models less picky about how you feed them data, and it help them learn quicker.
Layer normalization is especially good for models that think step by step, because it keeps the hidden signals steady over time, so stable hidden states are easier to keep.
The trick gives each neuron a small shift and scale after the fix, that way the network keeps its power while staying calm.
Because it works per example, there is no batch dependence, and it can be added to many kinds of models without big changes.
In short, layer normalization makes training faster, more reliable, and it works well in recurrent networks, so models learn better with less fuss.
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Layer Normalization
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