ML0047 Parameters

Parameters are the values that a model learns from its training data, while hyperparameters are settings defined by the user that guide the training process and model architecture.

Parameters:
(1) Internal variables learned from data (e.g., weights and biases).
(2) Adjusted during training using optimization algorithms.
(3) Capture the model’s learned patterns and information.

Hyperparameters:
(1) External configurations set before training (e.g., learning rate, batch size, number of layers).
(2) Remain fixed during training and are not updated by the learning process.
(3) Influence how the model learns and its overall structure.