sigmoid function
A mathematical function that "squishes" an input value into a constrained range, typically 0 to 1 or -1 to +1.
Plain English Explanation
A mathematical function that "squishes" an input value into a constrained range, typically 0 to 1 or -1 to +1. That is, you can pass any number (two, a million, negative billion, whatever) to a sigmoid and the output will still be in the constrained range. A plot of the sigmoid activation function looks as follows: The sigmoid function has several uses in machine learning, including: - Converting the raw output of a logistic regression or multinomial regression model to a probability. - Acting as an activation function in some neural networks.
The sigmoid function over an input number x has the following formula:
In machine learning, x is generally a weighted sum. ---
How is it used?
Practitioners refer to sigmoid function when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.