Hyperparameters

Hyperparameters are external to the model and are set before the training process begins, influencing how the model learns from data. Examples include the learning rate, batch size, and the number of layers in a neural network. Tuning hyperparameters optimally is vital for achieving the best model performance, often requiring methods like grid search or random search.