In the lecture # Vectorizing Logistic Regression’s Gradient Output
I got a little confused about the meaning of x here. There are x(i) and x1(i) and x2(i). If x1(i) and x2(i) stand for the attributes of example i, shouldn’t the z function look like this: z(i) = w1Tx1(i) + w2Tx2(i) + b, instead of z(i) = wTx(i) + b?