Editing Backpropagation (section)

===Example loss function===
Let <math>y,y'</math> be vectors in <math>\mathbb{R}^n</math>.

Select an error function <math>E(y,y')</math> measuring the difference between two outputs. The standard choice is the square of the [[Euclidean distance]] between the vectors <math>y</math> and <math>y'</math>:<math display="block">E(y,y') = \tfrac{1}{2} \lVert y-y'\rVert^2</math>The error function over <math display="inline">n</math> training examples can then be written as an average of losses over individual examples:<math display="block">E=\frac{1}{2n}\sum_x\lVert (y(x)-y'(x)) \rVert^2</math>