Hyperparameters

Hyperparameters are settings or configurations used in machine learning models that dictate how the model learns from data. Unlike regular parameters, which are adjusted automatically during training, hyperparameters must be set before the learning process begins. They can influence the model's performance significantly, affecting aspects like learning speed, complexity, and how well the model generalizes to new data. Examples of hyperparameters include the learning rate, number of layers in a neural network, and the batch size for processing data. Choosing the right hyperparameters is crucial for achieving optimal results.