feed-forward neural networks this leads to the following binary cross-entropy functionn yi log xi thusthe idea of maximum likelihood enables us to derive the crossentropy functionwhich can be used as loss function in the context of multi-classification squared error let us now discuss deriving the squared error to be used in the context of regression using maximum likelihood let us assume that unlike the previous caseswhere we assumed that we had model that predicted probabilitywe will assume that we have model that predicts the value of to apply the maximum likelihood ideawe assume that the difference between the actual and the predicted has gaussian distribution with zero mean and variance of thenit can be shown that minimizing = leads to the minimization of (thsummary of loss functions we now summarize three key points with respect to loss functions and the appropriateness of particular loss function given the problem at hand the binary cross-entropy given by the expression yi log xi yi log xi 