Documentation
English
Search…
⌃K

Useful resources

Academic papers

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 Experiments

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

How-to-guides

The following how-to-guide demonstrates how to solve real-world problems with Weights & Biases:
    • Description: How to use W&B Sweeps for hyperparameter tuning using XGBoost.

Sweep GitHub repository

Weights & Biases 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 Weights & Biases open source repo, see the W&B GitHub Contribution guidelines.