This requires two components that we foreshadowed in the introduction to the chapter. The first is a metric for how close the current label (ŷ) is to the true gold label y. Rather than measure similarity, we usually talk about the opposite of this: the distance between the system output and the gold output, and we call this distance the loss function or the cost function. In the next section we'll introduce the loss loss function that is commonly used for logistic regression and also for neural networks,