Python Deep Learning - Second Edition

Logistic regression

Logistic regression uses logistic sigmoid activation, in contrast to linear regression, which uses the identity function. As we've seen before, the output of the logistic sigmoid is in the (0,1) range and can be interpreted as a probability function. We can use logistic regression for a 2-class (binary) classification problem, where our target, t, can have two values, usually 0 and 1 for the two corresponding classes. These discrete values shouldn't be confused with the values of the logistic sigmoid function, which is a continuous real-valued function between 0 and 1. The value of the sigmoid function represents the probability that the output is in class 0 or class 1:

1. Let's denote the logistic sigmoid function with ...