The Deep Learning Tuning Playbook provides systematic approaches for maximizing performance of deep learning models. Authored by Google Research Brain Team members, it covers guide for starting new projects, choosing model architectures and optimizers, hyperparameter tuning methodology, scientific approaches to experimentation, and strategies for determining training duration for both compute-bound and non-compute-bound scenarios.