This document helps you train deep learning models more effectively. Although this document emphasizes hyperparameter tuning, it also touches on other aspects of deep learning training, such as training pipeline implementation and optimization.
This document assumes your machine learning task is either a supervised learning problem or a similar problem (for example, self-supervised learning) That said, some of the advice in this document may also apply to other types of machine learning problems.
We've aimed this document at engineers and researchers with at least a basic knowledge of machine learning and deep learning. If you don't have that background, please consider taking Machine Learning Crash Course.
Why did we write this document?
Currently, there is an astonishing amount of toil and guesswork involved in getting deep neural networks to work well in practice. Even worse, the actual recipes people use to get good results with deep learning are rarely documented. Papers gloss over the process that led to their final results in order to present a cleaner story, and machine learning engineers working on commercial problems rarely have time to take a step back and generalize their process. Textbooks tend to eschew practical guidance and prioritize fundamental principles, even if their authors have the necessary experience in applied work to provide useful advice.
When preparing to create this document, we couldn't find any comprehensive attempt to actually explain how to get good results with deep learning. Instead, we found snippets of advice in blog posts and on social media, tricks peeking out of the appendix of research papers, occasional case studies about one particular project or pipeline, and a lot of confusion. There is a vast gulf between the results achieved by deep learning experts and less skilled practitioners who are using superficially similar methods. However, the experts readily admit that some of what they do might not be well-justified. As deep learning matures and has a larger impact on the world, the community needs more resources covering useful recipes, including all the practical details that can be so critical for obtaining good results.
We are a team of five researchers and engineers who have worked in deep learning for many years, some of us since as early as 2006. We have applied deep learning in everything from speech recognition to astronomy. This document grew out of our own experience training neural networks, teaching new machine learning engineers, and advising our colleagues on the practice of deep learning.
It has been gratifying to see deep learning go from a machine learning approach practiced by a handful of academic labs to a technology powering products used by billions of people. However, deep learning is still in its infancy as an engineering discipline, and we hope this document encourages others to help systematize the field's experimental protocols.
This document came about as we tried to crystallize our own approach to deep learning. Thus, it represents our opinions at the time of writing, not any sort of objective truth. Our own struggles with hyperparameter tuning made it a particular focus of our guidance, but we also cover other important issues we have encountered in our work (or seen go wrong). Our intention is for this work to be a living document that grows and evolves as our beliefs change. For example, the material on debugging and mitigating training failures wouldn't have been possible for us to write two years ago because it is based on recent results and ongoing investigations.
Inevitably, some of our advice will need to be updated to account for new results and improved workflows. We don't know the optimal deep learning recipe, but until the community starts writing down and debating different procedures, we cannot hope to find it. To that end, we would encourage readers who find issues with our advice to produce alternative recommendations, along with convincing evidence, so we can update the playbook. We would also love to see alternative guides and playbooks that might have different recommendations so we can work towards best practices as a community.
About that robot emoji
The robot 🤖 emoji indicates areas where we would like to do more research. Only after trying to write this playbook did it become completely clear how many interesting and neglected research questions can be found in the deep learning practitioner's workflow.