google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.
github.com · 9,283 words · saved by 4 readers
A playbook for systematically maximizing the performance of deep learning models.
This is not an officially supported Google product. Varun Godbole†, George E. Dahl†, Justin Gilmer†, Christopher J. Shallue‡, Zachary Nado† † Google Research, Brain Team ‡ Harvard University Table of Contents Who is this document for? Why a tuning playbook? Guide for starting a new project Choosing the model architecture Choosing the optimizer Choosing the batch size Choosing the initial configuration A scientific approach to improving model performance The incremental tuning strategy Exploration vs exploitation Choosing the goal for the next round of experiments Designing…
saved by
related reading
- GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models. · GitHubgithub.com
- A Recipe for Training Neural Networkskarpathy.github.io
- How To Scale Your Modeljax-ml.github.io
- Making Deep Learning go Brrrr From First Principleshorace.io
- The Practitioner’s Guide to the Maximal Update Parameterization - Cerebrascerebras.ai
- Debugging Reinforcement Learning Systemsandyljones.com
- The Practitioner's Guide to the Maximal Update Parameterization | EleutherAI Blogblog.eleuther.ai
- The Little Book of Deep Learningfleuret.org
- A Recipe for Training Neural Networkskarpathy.github.io
- Hyperparameter optimization - Wikipediaen.wikipedia.org
- The Annotated Transformernlp.seas.harvard.edu
- Tiny TPUtinytpu.com