Hyperparameter Tuning

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Hyperparameter tuning is the process of adjusting the settings that control how an AI model learns from data, rather than the data itself. These settings determine how quickly and effectively the model adapts during training. Since they aren’t learned automatically, they must be selected manually or through automated search methods.

The process involves testing different combinations of settings and comparing how well the model performs on validation data to find the most effective setup. It can be as simple as trial and error or use more advanced techniques like grid search or Bayesian optimization. Hyperparameter tuning fine-tunes the model’s learning process to achieve the best possible results without wasting time or computing resources.

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