Hyperparameter
Configuration settings for model training that are set before training, not learned during training.
In more detail
Hyperparameters include learning rate, batch size, number of epochs, regularization strength, and architecture choices. Unlike parameters (learned), hyperparameters must be set manually or through techniques like grid search or Bayesian optimization.
Examples
- Learning rate
- Batch size
- Number of layers
- Dropout rate
- Regularization coefficient
Related terms
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