Hyperparameter

A parameter that controls the learning process and is set before training, not learned from data. Examples: learning rate, number of hidden layers, regularization strength, batch size. Tuning hyperparameters is a meta-optimization problem - it is done outside the training loop using cross-validation or a held-out validation set.

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