Linear Regression, Machine Learning, Introduction, Gradient Descent.
squared error (MSE). The MSE is defined by calculating the squared differences between real (y) and predicted (𝑦̂) values, which are then summed up and finally divided by the length of our dataset (N). Mean squared error We can consider the MSE as a function, depending on the (trainable) parameters