Parameter
A Parameter is a value learned by a machine learning model during training that determines how the model transforms input data into predictions or outputs.
What is a Parameter?
Parameters are internal values, such as weights and biases in neural networks, that are adjusted during training to minimize a loss function or improve the model's performance. The number and arrangement of parameters vary depending on the model architecture.
Why is Parameter Important?
Parameters determine how a trained model responds to different inputs and influence its ability to learn patterns from data. Managing and optimizing parameters is therefore central to model training, performance, and computational requirements.
Common use cases
Parameters are used across machine learning and deep learning models for classification, regression, natural language processing, computer vision, generative AI, and predictive modeling.