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Data scaling for large dynamic range in neural networks

Data Science Asked on April 4, 2021

The usual strategy in neural networks today is to use min-max scaling to scale the input feature vector from 0 to 1. I want to know if the same principle holds true if our inputs have a large dynamic range (for example, there may be some very large values and some very small values). Isn’t it better to use logarithmic scaling in such cases?

One Answer

If it is a classification problem, then you will use sigmoid or softmax to make the output value in (0,1) and all the value must sum to 1 as per the rule of probability.

Answered by SrJ on April 4, 2021

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