A logistic function is a form of sigmoid function often used as a weak threshold in an neural network. Like a step function it maps unbounded values to a finite [0,1] range and is zero for large negative values and one for large postive values. However, unlike a step function the progress is smooth with a near linear section in the middle and gently asymptotes for more extreme values. This is important for machine learning as, in general, it is easier to learn continuous features.
Used in Chap. 6: page 76
Logistic function equation and graph