I was confused because in weeks 1 and 2, we were told that fw,b(x) was actually wx + b (where w and x are either scalars or vectors, depending on whether this is univariate linear regression or multi linear regression).

But now this week, I’m now told here that fw,b(x) is g(z).

I think this is my limited past experience in advanced math coming up here. And all I need is a bit of background. I was confused because how is fw,b(x) both?

Now it’s dawning on me that maybe fw,b(x) is just an abstraction and what the course is saying is:

In the case of linear regression, fw,b(x) = wx+b

In the case of logistic regression, fw,b(x) = g(z) which happens to also use wx+b, in this case in the form of z.

Do I have that right?

Thank you for your patience explaining what seems to be such a basic core concept.

Yes you did explain but I wanted to explain to him in his terms of understanding statement, as he had a correct understanding but was confused, so I explained using his mentioned statements.

I hope this is not a problem. if yes, then extremely sorry.

I understood independently the two different calculations for for linear and logistic regressions, that is, the difference between the linear and sigmoid functions. I also understood that I was doing linear regression in weeks 1 and 2 and logistic regression in week 3.

I didn’t need those explained to me. But perhaps I didn’t make that clear enough.

My confusion was specifically how f(x) somehow meant both. But it doesn’t mean both…rather it means either, or rather (as I understand it) it’s an abstract notation which needs to be defined for each.