What are hyperparameters and how are they different from parameters?

Hyperparameters vs. Parameters:

Hyperparameters are settings or configurations of a machine learning model that are set prior to training. Examples include learning rates and regularization strengths.

Parameters are the internal variables of the model that are learned from the training data. They define the model's behavior, such as weights and biases in a neural network.