Once forward propagation is complete, we have the network's prediction for each data point. We also know that data point's actual value. Typically, the prediction is defined as 
while the actual value of the target variable is defined as y.
Once both y and 
are known, the network's error can be computed using the cost function. Recall that the cost function is the average of the loss function.
In order for learning to occur within the network, the network's error signal must be propagated backwards through the network ...
