- parameters - are parameters learned over the training-set
- hyperparameters - are parameters that control the model capacity (is not to learned over training-set, as it would always choose values resulting in maximum capacity and thus overfitting). Examples include:
- number of epochs
- learning rate
- model architecture/size
- loss function