Parameters and Hyperparameters

Parameters are the numerical values a model learns from data during training, such as weights and bias terms in neural networks. Hyperparameters, by contrast, are settings chosen externally, such as learning rate, batch size, number of layers, or regularization strength. This distinction matters because parameters determine the model’s internal behavior, while hyperparameters determine how that behavior is learned. Put simply, one relates to what the model learns, and the other to how it learns it. Building a strong AI system requires not only good data but also strong discipline in hyperparameter design and tuning.