Performance of machine learning systems depends critically on tuning parameters that are difficult to set by standard optimization techniques. Such “hyperparamers”—including model architecture, regularization, and learning rates—are often tuned in an outerloop by black-box search methods evaluating performance on a holdout set. We formulate such hyperparameter tuning as a pure-exploration problem of deciding how many resources should be allocated to particular hyperparameter configurations. I will introduce our Hyperband algorithm for this framework and a theoretical analysis that demonstrates its ability to adapt to uncertain convergence rates and the dependency of hyperparameters on the validation loss. I will close with several experimental validations of Hyperband, including experiments on training deep networks where Hyperband outperforms state-of-the-art Bayesian optimization methods by an order of magnitude. I will also describe a highly scalable and asynchronous version of Hyperband I implemented and validated at Google.