function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)
%GRADIENTDESCENTMULTI Performs gradient descent to learn theta
%   theta = GRADIENTDESCENTMULTI(x, y, theta, alpha, num_iters) updates theta by
%   taking num_iters gradient steps with learning rate alpha

% Initialize some useful values
m = length(y); % number of training examples
J_history = zeros(num_iters, 1);

for iter = 1:num_iters

    % ====================== YOUR CODE HERE ======================
    % Instructions: Perform a single gradient step on the parameter vector
    %               theta. 
    %
    % Hint: While debugging, it can be useful to print out the values
    %       of the cost function (computeCostMulti) and gradient here.
    %

    Update = 0;
    for i=1:m,
        Update = Update + alpha/m * (theta' * X(i,:)' - y(i)) *  X(i,:)';
    end

    theta = theta - Update;		

    % ============================================================

    % Save the cost J in every iteration    
    J_history(iter) = computeCostMulti(X, y, theta);

end

end

1094401996
github-repos/MATLAB/1094401996-machine-learning-coursera/machine-learning-coursera-e53d1021a08b0f2ab7e0840d9807ab14e24ea9bb/problem_sets/ex1_solution/gradientDescentMulti.m
machine-learning-coursera
github-repos/MATLAB/1094401996-machine-learning-coursera
e53d1021a08b0f2ab7e0840d9807ab14e24ea9bb