What is value normalization in neural networks

This recipe explains what is value normalization in neural networks

Recipe Objective - What is value normalization in neural networks?

Normalization is the process of transforming data in a way that the data has similar dimensions or either the data is dimensionless. Normalization prevents the large gradient updates that is it will prevent the network from converging. Data or values in normalization should be of smaller values ranging from 0 - 1 and should be homogenous that is the features should take the values in roughly the same range. Value normalization helps in making comparisons easy among the features by giving the same dimensions.

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Explanation of value normalization.

In value normalization, image data is encoded as integers in the 0–255 range, encoding grayscale values. Before the data is fed into the network, had to divide by 255 so you’d end up with floating-point values in the 0–1 range.

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I come from a background in Marketing and Analytics and when I developed an interest in Machine Learning algorithms, I did multiple in-class courses from reputed institutions though I got good... Read More

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