Hyperparameters are variables that control a model training process. They include:
- The learning rate.
- The number of layers in a neural network.
- The number of nodes in each layer.
Hyperparameter values are not learned. In contrast to the node weights and other training parameters, a model training process does not adjust hyperparameter values.