Derivative of Sigmoid for BackpropagationUnsolved
Derivatives are used in the process of backpropagation. The optimal set of values are computed by gradient descent, which uses the derivative of the sigmoid function, because that is the activation used by the output neuron in a neural netowkr.
Given an input value to a neuron, find "the ability of neuron to learn" by calculating the derivative of the sigmoid function on that specific input value.
Note: You can use the math.exp builtin function for calculating the sigmoid value.
Input to Neuron (x) = -2
Derivative of Sigmoid = 0.1049935854035065
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numpy has been already imported as np (import numpy as np)