deep neural network model Phi literally refers to the weights the D refers to your training data so it's a set of tuples of input-output pairs where the input might be something like an image and the output is maybe the label corresponding to the class of the object in that image now when we actually want to do this kind of maximum like that estimation problem we typically apply Bayes rule and rewrite it as the sum of log P of D given Phi plus log P of Phi and the first term is typically referred to as the likelihood of your data and