num_pages: 424
description: Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively.SummaryMost machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems. This can leave a big knowledge gap for data scientists working in real-world machine learning, where data scientists spend more time on data management than on building algorithms. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the technologyMachine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster.About the bookHuman-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. You’ll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You’ll learn to create training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.What's inside    Identifying the right training and evaluation data    Finding and managing people to annotate data    Selecting annotation quality control strategies    Designing interfaces to improve accuracy and efficiencyAbout the authorRobert (Munro) Monarch is a data scientist and engineer who has built machine learning data for companies such as Apple, Amazon, Google, and IBM. He holds a PhD from Stanford.Robert holds a PhD from Stanford focused on Human-in-the-Loop machine learning for healthcare and disaster response, and is a disaster response professional in addition to being a machine learning professional. A worked example throughout this text is classifying disaster-related messages from real disasters that Robert has helped respond to in the past.Table of ContentsPART 1 - FIRST STEPS1 Introduction to human-in-the-loop machine learning2 Getting started with human-in-the-loop machine learningPART 2 - ACTIVE LEARNING3 Uncertainty sampling4 Diversity sampling5 Advanced active learning6 Applying active learning to different machine learning tasksPART 3 - ANNOTATION7 Working with the people annotating your data8 Quality control for data annotation9 Advanced data annotation and augmentation10 Annotation quality for different machine learning tasksPART 4 - HUMAN–COMPUTER INTERACTION FOR MACHINE LEARNING11 Interfaces for data annotation12 Human-in-the-loop machine learning products
language: English
title: Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI
publisher: Manning