Weights and biases are adjusted by an optimizer using gradients or another learning rule. Parameter count describes size but does not by itself determine capability, speed, or quality.
A model parameter is a value learned from training data that influences the model's predictions.
Weights and biases are adjusted by an optimizer using gradients or another learning rule. Parameter count describes size but does not by itself determine capability, speed, or quality.