Linear Regression, Gradient Descent, Machine Learning, Mathematics, Data Analysis.
Function The mean squared error (cost function) finds the average squared difference between our predictions, and the actual y values in our training set. Our goal is to minimize the total cost. For each training example, MSE adds up the lengths of all the dashed lines, which are the differences