C1_W3_Lab02_Sigmoid_function_Soln - How is 'z' calculated?

β€œNote, the orange line is β€˜π‘§β€™ or 𝐰⋅𝐱(𝑖) + 𝑏 above. It does not match the line in a linear regression model.”

I don’t get how β€˜z’ is calculated and why it’s different from a linear regression model. Where do the β€˜w’ and β€˜b’ values come from to calculate β€˜z’ in this example?

Are we using linear regression to find the values β€˜w’ and β€˜b’, but each iteration of the algorithm performs the sigmoid function on β€˜z’?

We’re using gradient descent to find the best w and b values.

The gradients for logistic regression look quite similar to those for linear regression, other than including sigmoid() when computing f_wb.

Is the sigmoid function what makes z turn out different in this lab?

Via some calculus magic, the form of the gradients are the same as for the linear regression cost function, if viewed in terms of the f_wb value.