Definition and interpretation of z in logistic regression

Great sharing! @shanup presented the convenience of having z in calculus; @Shivanshu_Singh told us Professor Andrew Ng’s geometric interpretation of the polynomial function z as a decision boundary (course 1 week 3 Video: Decision Boundary, and a relevant discussion); my last point is, mathematically, z is also called a logits in our context (logistic regression), so your wx+b projects x to logits z and your sigmoid function projects z to a probability. logits is a name you will see again in course 2 week 2.