Hyperparameters
In the context of machine learning, hyperparameters are parameters that are not learned from data but are set prior to training. They are used to control the learning process and affect the performance of the model. Examples of hyperparameters include learning rate, regularization strength, number of hidden layers, and number of neurons in each layer. Hyperparameters are usually set through trial and error or using techniques such as grid search or random search. The choice of hyperparameters can have a significant impact on the performance of the model, and finding the optimal values is an important part of the machine learning workflow.
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