In Depth
Hyperparameters are configuration values set before training begins, as opposed to parameters (weights) that the model learns during training. Examples include learning rate, batch size, number of layers, dropout rate, and regularization strength. Hyperparameter tuning is the process of systematically searching for the combination that produces the best model performance.
Common tuning strategies range from simple to sophisticated. Grid search exhaustively tries every combination from a predefined set. Random search samples combinations randomly, which often finds good solutions faster than grid search. Bayesian optimization uses previous results to intelligently suggest promising configurations. More advanced approaches like Hyperband and population-based training combine multiple strategies for efficiency.
For large language models and deep learning, hyperparameter tuning can be extremely expensive since each configuration requires a full training run. This has led to the development of learning rate schedulers, warmup strategies, and transfer of hyperparameters across similar tasks. Tools like Ray Tune, Optuna, and Weights & Biases automate and track hyperparameter experiments at scale.