What is the purpose of a cost function in an artificial neural network?

The purpose of a cost function in an artificial neural network is to measure the error and guide adjustments during the learning process. This function quantifies how well the model's predictions align with the actual target values by calculating the difference or "error" between the predicted output and the true output. By evaluating this error, the cost function provides essential feedback that helps the model improve its performance through optimization techniques, such as gradient descent. As the training progresses, the goal is to minimize the cost function, leading to better predictions and a more accurate model overall.

The other options do not accurately reflect the role of the cost function. Determining model complexity does not directly involve assessing errors; evaluating customer satisfaction is a separate metric unrelated to neural network training; and increasing the speed of training is influenced by various techniques such as batch size and learning rate rather than the function that measures error.