What is hyperparameter tuning in deep learning?

The question is about deep learning

Answer

Hyperparameter tuning in deep learning involves optimizing the parameters that govern the training process and model architecture. These parameters include learning rate, batch size, number of layers, and activation functions. Proper tuning is crucial for achieving optimal model performance, as it affects convergence speed and accuracy. Techniques like grid search, random search, and Bayesian optimization are used to explore the hyperparameter space. Effective tuning can significantly enhance model performance, leading to better generalization and predictive accuracy.

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