Regression with a perceptron and derivatives

I'm taking the Deeplearning.AI course; Calculus for Machine Learning and Data Science as a refresh between semesters in my masters at Georgia Tech.

We covered gradient descent, and how derivatives and cost functions make it work.

I understand y and yHat are part of L(y,yhat) but no idea why he picked different parts out.

As he's explaining linear regression using gradient descent, I can't figure out why he's doing derivatives like this.

Posted Apr 3, 2026, 4:53 PM UTC