Hands-On Mathematics for Deep Learning

Mean squared error

Mean squared error (MSE) is one of the most commonly used loss functions, especially for regression tasks (it takes in a vector and outputs a scalar). It calculates the square of the difference between the output and the expected output. It looks as follows:

Here, N is the number of samples in our training dataset.

In the preceding equation, we calculate the square of the L2 norm. Intuitively, we should be able to tell that when , the error is 0, and the larger the distance between the points, the larger the error. The reason ...