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The sigmoid function generates a smooth non-linear curve that squashes the incoming values between 0 and 1. The sigmoid function works well for a classifier model but it has problems with vanishing gradients for high input values, that is, y change very slow for high values of x.
Example: If you have input values x of [1, 3, 10, 500, 10000, 10000000], y will change well enough for the lower values but not for the high values. The information in the high values will, therefore, be lost.