iteration

A single update of a model's parameters—the model's weights and biases—during training. The batch size determines how many examples the model processes in a single iteration. For instance, if the batch size is 20, then the model processes 20 examples before adjusting the parameters. When training a neural network, a single iteration involves the following two passes: 1. A forward pass to evaluate loss on a single batch. 2. A backward pass (backpropagation) to adjust the model's parameters based on the loss and the learning rate. See Gradient descent in Machine Learning Crash Course for more information.

Practitioners refer to iteration when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.