h = x.*theta; diff = h-y; theta = theta-(alpha/m)*x'*diff; in matlab

To implement the given equation in MATLAB, you can follow these steps:

main.m
% Assuming x, theta, and y are already defined with appropriate sizes and values

m = length(y); % number of training examples
alpha = 0.01; % learning rate

h = x * theta; % matrix multiplication of x and theta
diff = h - y; % calculate the difference between h and y
theta = theta - (alpha/m) * x' * diff; % update theta using gradient descent
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In this code snippet, x represents the input feature matrix, theta represents the parameter matrix, and y represents the target variable.

Here are the explanations for each step:

• h = x * theta performs matrix multiplication between x and theta. This calculates the hypothesis h for each training example.
• diff = h - y calculates the difference between the hypothesis h and the actual target values y.
• theta = theta - (alpha/m) * x' * diff updates the parameter matrix theta using gradient descent. The x' is the transpose of the x matrix, diff is the difference vector, and (alpha/m) is the learning rate and normalization constant.

Make sure that x and theta have the appropriate dimensions to perform matrix multiplication and element-wise operations.

Please note that this code assumes that the dimensions of x and theta are compatible for matrix multiplication.

The code snippet can be modified and adjusted based on your specific problem and data structures.