In deep learning and machine learning, models are typically built using parametric forms. This means that a model's behavior is defined by a mathematical function that relies on a set of adjustable parameters, such as weights and biases. Instead of explicitly programming the exact rules to solve a problem, the learning algorithm iteratively updates these parameters to best approximate the underlying patterns in the training data. While simple models like linear regression provide a clear introduction to parametric forms, this foundational concept is an essential component required by all modern neural network architectures.