Why it matters

Wrong hyperparameters waste training. Efficient tuning matters.

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The architecture

Grid: exhaustive. Random: often better. Bayesian: Ax, Optuna.

Hyperparameter tuningRandom searchoften bestBayesianAx / OptunaPopulation-basedPBTRandom search often beats grid; Bayesian better than both
Tuning strategies.
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How it works end to end

Early stopping: kill bad runs.

Multi-fidelity: cheap approximations.

PBT: population-based training.

Frameworks: Ray Tune, Optuna, Ax.