Editing Backpropagation (section)

===Assumptions===
The mathematical expression of the loss function must fulfill two conditions in order for it to be possibly used in backpropagation.<ref>{{harvtxt|Nielsen|2015}}, "[W]hat assumptions do we need to make about our cost function ... in order that backpropagation can be applied? The first assumption we need is that the cost function can be written as an average ... over cost functions ... for individual training examples ... The second assumption we make about the cost is that it can be written as a function of the outputs from the neural network ..."</ref> The first is that it can be written as an average <math display="inline">E=\frac{1}{n}\sum_xE_x</math> over error functions <math display="inline">E_x</math>, for <math display="inline">n</math> individual training examples, <math display="inline">x</math>. The reason for this assumption is that the backpropagation algorithm calculates the gradient of the error function for a single training example, which needs to be generalized to the overall error function. The second assumption is that it can be written as a function of the outputs from the neural network.