I just finished Week 3’s problem set without a hitch. The instructions were clear and I found the math itself to be pretty straightforward.
What I’m trying to ascertain is bringing it all together. Specifically, when we are running these models, how does the graph result in multiple decision boundaries for the different zones of the data?
If we were using say, a gaussian kernel solution the result would be pretty clear, but the intuition behind what we’re calculating to get the final result is a bit lost on me.
Does anybody have an ‘explain like I’m five’ way to communicate what the model is doing?
Great course by the way!