Definition
In machine learning, a hyperparmater is a configuration external to the model whose value is set prior to the commencement of the learning process. These variables control the behavior of the training algorithm and the structure of the model, distinguishing them from parameters which are learned directly from data.
Examples
Common examples include the learning rate in gradient descent and the number of hidden layers in a neural network.