Parameters and Weights

In the context of neural networks, parameters and weights are the internal, numerical variables that the model optimizes during the learning process to map the relationships between input data and desired output. Weights determine the strength of connections between individual neurons, influencing how much information is transmitted from one layer of the network to another, while biases set the activation threshold. The precise fine-tuning of these values enables artificial intelligence to acquire generalizable knowledge, increasing the model's accuracy in inference on unknown data. Overall, the number of parameters — which can reach the scale of trillions in modern LLMs — is the primary measure of a model's complexity and capacity.