Gradient Descent For Multiple Variables Octave. PlotDatam - Function to display the dataset. It will work the same with multiple features since all that happens is you add an extra column to your X matrix for each feature.
Updating the parameters parameters1 parameters1 - learningRate 1m h x 1. Assuming x_0 1 theta_jtheta_j alpha frac1m sum_i1m h_thetaxi yi. Theta 0 theta 0 - alpha m X theta 0 - y.
Theta theta - alpha m X theta - y Xthis is the answerkey provided.
Gradient descent will take longer to reach the global minimum when the features are not on a similar scale. GradientDescentm - Function to run gradient descent. My answer key theta 1 theta 1 - alpha m X theta. In Octave you can multiply xji to all the predictions using so it can be written as.
