feed-forward neural networks have somehow generated model that predicts the probability of given we denote this model by (xth)where th represents the parameters of the model let us again use the idea behind maximum likelihoodwhich is to find th that maximizes (dthassuming multinomial distributionand given that each example {( )( )(xnyn)is independentwe have the following expressionp ny xi nk we can take logarithm operation on both sides to arrive at the followingn log log log !nk log xi this can be simplified to the followingn log log log !nk yi log xi the terms log nand log nkare not parameterized by th and can be safely ignored as we try to find th that maximizes (dththuswe have the followingn log yi log xi as beforeinstead of maximizing the rhswe minimize its negative valueas followsn yi log xi