The difference between predicted and expected values when training a machine learning model. The "gradient descent" is an example of an optimization algorithm used to derive the smallest difference in the cost function, which is the goal. This is achieved by using "backpropagation," which means keep going back to the beginning and readjusting all the parameters between layers in the network (see
neural network). See
AI weights and biases and
large language model.