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This video introduces the motivation for using conditional random fields. It contrasts this with neural networks, where a single input goes through hidden layers to produce a pre-activation vector. The softmax function creates a distribution over potential labels. This distribution represents the network's beliefs about the input's label likelihood. By classifying inputs based on the most likely label according to the network, predictions can be made. When presented with a new input, the process is repeated to compute hidden layers, activation vectors, and softmax outputs.
