Hyperparameter Tuning

Hyperparameter tuning is the process of selecting the optimal set of hyperparameters for a machine learning algorithm to achieve better performance on a given task. Hyperparameters are parameters that are not learned from data, but rather set by the user before training the model. Examples of hyperparameters include learning rate, regularization strength, number of hidden layers, and number of neurons in each layer. The process of hyperparameter tuning involves selecting a range of values for each hyperparameter, training the model with different combinations of hyperparameters, and evaluating the performance of the model on a validation set. The goal is to find the set of hyperparameters that results in the best performance on the validation set. Hyperparameter tuning can be done manually or using automated techniques such as grid search, random search, and Bayesian optimization.
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