In artificial intelligence and machine learning, a parameter is a variable that a model learns from data during the training process. Parameters are the internal values that define how a model transforms inputs into outputs. For example, in a neural network, parameters usually refer to the weights and biases that connect the layers together and determine the strength of the connections between neurons.
Think of parameters as the dials and switches inside a machine learning model. During training, the model adjusts these dials based on the data it sees, trying to find values that minimize errors and improve predictions. For instance, in linear regression, the slope and intercept are parameters. In more complex models like deep neural networks, there can be millions or even billions of parameters.
Parameters are not set manually; instead, they are automatically adjusted by optimization algorithms such as gradient descent. The model starts with random parameter values, makes predictions, checks how far off it was, and then tweaks the parameters to get closer to the correct answers. This process repeats over many iterations until the parameters settle into values that enable the model to make accurate predictions on new, unseen data.
It’s important to distinguish parameters from hyperparameters. While parameters are learned during training, hyperparameters are set before training begins and control aspects of the training process itself, such as the learning rate or the number of layers in a neural network.
Parameters play a crucial role in determining a model’s performance. Too few parameters can lead to underfitting, where the model can’t capture the complexity of the data. Too many parameters might result in overfitting, where the model learns noise instead of the actual patterns. Regularization techniques are often used to manage the number and influence of parameters to improve generalization.
When people discuss the size or power of a model, they often refer to the number of parameters it contains. For example, large language models like GPT have hundreds of billions of parameters, allowing them to capture complex relationships in language. However, more parameters also mean more computational resources are required for training and inference.
Understanding parameters is key to grasping how machine learning models function, as they are the core components that adapt based on data and allow models to learn from experience.
