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Useful resources

Academic papersโ€‹

Li, Lisha, et al. "Hyperband: A novel bandit-based approach to hyperparameter optimization." The Journal of Machine Learning Research 18.1 (2017): 6765-6816.

Sweep Experimentsโ€‹

The following W&B Reports demonstrate examples of projects that explore hyperparameter optimization with W&B Sweeps.

How-to-guidesโ€‹

The following how-to-guide demonstrates how to solve real-world problems with W&B:

Sweep GitHub repositoryโ€‹

W&B advocates open source and welcome contributions from the community. Find the GitHub repository at https://github.com/wandb/sweeps. For information on how to contribute to the W&B open source repo, see the W&B GitHub Contribution guidelines.

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