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Batch Normalization

Batch Normalization is a technique for improving the speed, performance, and stability of neural networks. It is used to normalize the input layer by adjusting and scaling the activations in order to provide similar scales for all inputs. Example: if the dataset includes the 2 features - age (0-100) and miles driven (0-100K) we would like to have them both in the same scale (0-1) in order to avoid instability.

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