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QWhat is the meaning of the term "Transfer Learning" in AI?
ID: #11123
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Hyperparameters are parameters that are set before the learning process in a machine learning model and affect its behavior and performance. These parameters are not learned from the data but are chosen by the practitioner or researcher. Examples of hyperparameters include learning rate, regularization strength, the number of hidden layers in a neural network, or the depth of a decision tree. Hyperparameter tuning is the process of finding the best combination of values for these parameters to optimize the model's performance.
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