What is the difference between parameters and hyperparameters?

Parameters are the internal values learned by the model during training from the data — for example, the weights and biases in a neural network, or the coefficients in linear regression. Hyperparameters are configuration settings set before training that control the learning process — for example, learning rate, number of epochs, number of hidden layers, batch size, regularization strength. Parameters are optimized automatically by the training algorithm; hyperparameters must be tuned manually or via techniques like grid search, random search, or Bayesian optimization.