cavis/docs/datavec/templates/normalization.md

title	short_title	description	category	weight
DataVec Normalization	Normalization	Preparing data in the right shape and range for learning.	DataVec	5

Why normalize?

Neural networks work best when the data they’re fed is normalized, constrained to a range between -1 and 1. There are several reasons for that. One is that nets are trained using gradient descent, and their activation functions usually having an active range somewhere between -1 and 1. Even when using an activation function that doesn’t saturate quickly, it is still good practice to constrain your values to this range to improve performance.

Available preprocessors

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