Hyperparameters of a model are the kind of parameters that cannot be directly learned during training but are set beforehand. Hyperparameters can define, for example, the complexity of the model or its capacity to learn.

Hyperparameters are model parameters that are defined before training begins whereas regular parameters are learned during the training process.