Hi, I just completed Course 1: Week3 + Assignment. During the assignment, I was surprised to see the difference in plots between standard logistic regression and our NN model (ie. number of sectional “cuts”. 1 vs k for logistic vs. our NN).

Course 1: Week 3: Assignment. #5.2 scatter plot vs. #2 scatter plot.

I wanted to make sure I’m understanding the intuition right (putting aside the model architecture of NN). If we manually expanded out A2 or y_hat (as a function of our input A0). We have a linear equation like so: W2W1A0 + b1W2 + b2 or (ax + bx + …+Lx + b1W2+…+…bL). [can simplify all the constant terms to just: ax+bx+…+Lx + B

Am I right that the reason for the NN model doing better is because of the fact that we basically have MULTIPLE linear equations (vs. 1 in logistic) that contribute to the prediction. And the loss that’s minimized is a function of their sum. Meaning sections where y=1 for example, is the contribution of the sum of those equations.

If I’m wrong, what’s the intuition behind the fact that we’re getting these sectional cuts in plot