Answer Posted / Adarsh Prajapati

Parameters are the variables learned by a machine learning algorithm during training to minimize the loss function. They are specific to a particular model and are updated based on the data. Hyperparameters, on the other hand, are the variables that determine the behavior of the learning algorithm itself, such as learning rate or number of layers in a neural network. These values are typically set before training starts and are not updated during the training process.

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