In machine learning, hyperparameters are user-defined, tunable settings that govern the structure or training process of a model but are not updated within the primary training loop itself. Examples include the number of epochs (max_epochs), the minibatch size (batch_size), and the learning rate (lr). Because these values are not optimized by the standard training algorithm along with the model's weights and biases, they must be specified beforehand. Despite not being learned during training, they still significantly influence the performance of the model, affecting both optimization during training and generalization to unseen data.