Multiple Features (variables)

• Previously: f<w, b>(x) = wx + b

• Now: f<w, b>(x) = w1x1 + w2x2 + w3x3 + w4x4 + … + wnxn + b (Multiple Linear Regression)

Vectorization

Using vectorization will both make your code shorter and also make it run more efficiently

Vectorization how to work on our computer

Gradient descent for multiple regression

if 300 <= x_1 <= 2000

x_1, scaled = x_1 / 2000 (max)

0.15 (300 / 2000) <= x_1, scaled <= 1 (2000 / 2000)

Feature scaling

Mean Normalization

if you want to calaulate the mean normalization. first of all, you have to calculate the average (x1 on your training set), which is called Mu_1.

x_1 = x_1 - Mu_1 / Max - Min

For example Mu_1 = 600, 300 <= x_1 <= 2000, so -0.18 (300 - 600) / (2000 - 300) <= x_1 <= 0.82 (2000 - 600) / (2000 - 300)

Z-scope Normalization

Starndard deviation

Check Gridient Descent for Converging