Название: Human-in-the-Loop Machine Learning: Active learning, annotation and human-computer interaction (MEAP)
Автор: Robert Munro
Издательство: Manning Publications
Год: 2020
Формат: PDF
Страниц: 371
Размер: 25 Mb
Язык: English

“Human-in-the-Loop machine learning” refers to the need for human interaction with machine learning systems to improve human performance, machine performance, or both. Most machine learning projects do not have the time or budget for human input on every data point, and so need strategies for deciding which data points are the most important for human review. Ongoing human involvement with the right interfaces expedites the efficient labeling of tricky or novel data that a machine can’t process, reducing the potential for data-related errors.
About the book
Human-in-the-Loop Machine Learning is a guide to optimizing the human and machine parts of your machine learning systems, to ensure that your data and models are correct, relevant, and cost-effective. 20-year machine learning veteran Robert Munro lays out strategies to get machines and humans working together efficiently, including building reliable user interfaces for data annotation, Active Learning strategies to sample for human feedback, and Transfer Learning. By the time you’re done, you’ll be able to design machine learning systems that automatically select the right data for humans to review and ensure that those annotations are accurate and useful.