Parameter - Page 6

A parameter is a numerical value learned by a model during training. Neural network weights and biases are parameters because the optimization process adjusts them to reduce prediction error. Together, they determine how input is transformed into output and store much of the model’s learned behavior. Parameter count is often used to describe model size, but more parameters do not automatically mean better quality; architecture, data, training, and evaluation matter as well. Parameters differ from hyperparameters, which are chosen by developers to control training or structure. Updating parameters through fine-tuning can specialize a model, while compression and quantization change how they are stored or represented for deployment.