# Sigmoid Function

In data science and artificial intelligence, the sigmoid function is a mathematical function that maps any input value to a value between 0 and 1. It is commonly used as an activation function in artificial neural networks to introduce non-linearity into the output of a neuron. The sigmoid function is defined as f(x) = 1 / (1 + e^-x), where e is the mathematical constant approximately equal to 2.71828. The output of the sigmoid function can be interpreted as a probability, with values closer to 1 indicating a higher probability and values closer to 0 indicating a lower probability. The sigmoid function is also used in logistic regression to model the probability of a binary outcome based on input variables.

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