Let's consider the following linear regression example where we have a set of training data. Based on the training data, we use forward propagation to model a straight line prediction function, h(x), as in the following diagram:
Figure 4.11: Forward propagation to model a straight line function
The difference between the actual and predicted value for an individual training sample contributes to the overall error for the prediction function. The goodness of fit for a neural network is defined with a cost function. It measures how well a neural network performed with respect to the training dataset when ...
