Neural Network Projects with Python

The loss function

There are many available loss functions, and the nature of our problem should dictate our choice of loss function. For now, we'll use a simple Sum-of-Squares Error as our loss function:

The sum-of-squares error is simply the sum of the difference between each predicted value and the actual value. The difference is squared so that we measure the absolute value of the difference.

Our goal in training is to find the best set of weights and biases that minimizes the loss function.