What is hyperparameter tuning in deep learning?

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Answer

Hyperparameter tuning in deep learning involves optimizing the parameters that govern the training process of a model, such as learning rate, batch size, and number of layers. Proper tuning is crucial for achieving optimal model performance and avoiding overfitting or underfitting. Techniques like grid search, random search, and Bayesian optimization are commonly used to explore the hyperparameter space. Effective tuning requires balancing computational cost with performance gains, as it can be time-consuming. Automated tools and frameworks can assist in streamlining this process for efficient model training.

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