Hyperparameter Tuning

Hyperparameter Tuning is the process of searching for the optimal values of hyperparameters — configuration settings that are not learned during training but must be set before training begins. Unlike model parameters (weights), which are updated by the optimizer, hyperparameters control the training process itself and the model's architecture. Key hyperparameters include learning rate, batch size, number of layers, hidden dimension size, dropout rate, weight decay, and the number of training epochs.

Hyperparameters significantly impact model performance — a poorly chosen learning rate can mean the difference between a model that trains successfully and one that never converges. Common tuning strategies include grid search (exhaustively evaluating all combinations of a predefined set of values), random search (sampling hyperparameter values from defined distributions), and Bayesian optimization (building a probabilistic model of the hyperparameter-performance relationship to guide the search). Random search is often more efficient than grid search because not all hyperparameters are equally important.

Modern approaches also include population-based training (evolving hyperparameters during training), Hyperband (early termination of poorly performing configurations), and automated tools like Optuna and Ray Tune. Hyperparameter tuning is computationally expensive because each configuration requires a full (or partial) training run. Best practices include using validation data (never test data) for evaluation, starting with established recipes before tuning, and prioritizing the most impactful hyperparameters (learning rate, batch size, regularization strength) over less sensitive ones.