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Effective Amazon Machine Learning by Alexis Perrier

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Normalization

Machine learning algorithms incrementally update the model parameters by minimizing the error between the real value and the one predicted with the last iteration's parameters. To measure this prediction error we introduce the concept of loss functions. A loss function is a measure of the prediction error. For a certain algorithm, using different loss functions will create variants of the algorithm. Most common loss functions use the L2 or the L1 norm to measure the error:

●  L2 norm:

●  L1 norm:

Where yi and ŷ are the real and ...

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