Mean Squared Error

These are some additional notes that I am taking on the incredible book by David Foster on Generative Deep Learning

MSE stands for Mean Squared Error. The calculation for this is:

$$\Huge MSE = \frac{1}{n}\overset{n}{\underset{i = 1}{\Sigma}} (y_i - p_i)^2$$

This simply shows the difference between the ground truth ($y_i$) and what is predicted ($p_i$). This is typically used when trying to solve regression problems.