From the course: Deep Learning with Python: Optimizing Deep Learning Models
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Methods for hyperparameter tuning - Python Tutorial
From the course: Deep Learning with Python: Optimizing Deep Learning Models
Methods for hyperparameter tuning
- [Instructor] Hyperparameter tuning is a critical step in training deep learning models as it involves finding the optimal settings for hyperparameters that control the training process and the model architecture. Unlike model parameters, which are learned automatically during training, hyperparameters must be set before training begins and directly influence the model's performance, convergence, and generalization ability. The process of hyperparameter tuning can be challenging as a search space for hyperparameters is often vast, and their interactions can be complex. However, several effective methods are commonly used to identify the best hyperparameter configurations for a given problem. One of the simplest and most widely used methods is grid search. In grid search, a predefined set of values is specified for each hyperparameter, and the algorithmic value is the model's performance for all possible combinations of these values. This exhaustive search ensures that the optimal…
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