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in this video well introduce a motivation for using conditional random fields so what weve seen so far with neural networks is that they were a model that could take a single input and then through the computation of different hidden layers wed get at the output of the neural network a vector of pre activations and then by using the softmax non-linearity we could get a full distribution over what the potential label label of the actual inputs in this case this image could be so then based on this distribution which we can think of as what is the beliefs of the neural network about what is the most likely what is the likeliness of assigning X to any different label by using this distribution we could make a prediction by classifying the input with the class that is the most likely according to the neural network and then if we gave it a new or different input here then it would again compute all the hidden layers at the output layer get Appy activation vector and after the softmax g