Q. What are Hyperparameters? What is Hyperparameter tuning?

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Hyperparameters are parameters that are set before the learning process begins. Hyperparameter tuning is the process of selecting the best hyperparameters for a machine learning model.

Hyperparameters are not learned during the training process, but are set before training begins.
Examples of hyperparameters include learning rate, number of hidden layers in a neural network, and regularization strength.
Hyperparameter tuning involves selecting the best combination of hyperparameters to optimize the performance of a machine learning model.
Techniques for hyperparameter tuning include grid search, random search, and Bayesian optimization.

Q. What are Hyperparameters? What is Hyperparameter tuning?

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