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Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI
Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively.
Summary
Most 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 technology
Machine 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 book
Human-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 efficiency
About the author
Robert (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 Contents
PART 1 - FIRST STEPS
1 Introduction to human-in-the-loop machine learning
2 Getting started with human-in-the-loop machine learning
PART 2 - ACTIVE LEARNING
3 Uncertainty sampling
4 Diversity sampling
5 Advanced active learning
6 Applying active learning to different machine learning tasks
PART 3 - ANNOTATION
7 Working with the people annotating your data
8 Quality control for data annotation
9 Advanced data annotation and augmentation
10 Annotation quality for different machine learning tasks
PART 4 - HUMAN–COMPUTER INTERACTION FOR MACHINE LEARNING
11 Interfaces for data annotation
12 Human-in-the-loop machine learning products
Summary
Most 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 technology
Machine 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 book
Human-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 efficiency
About the author
Robert (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 Contents
PART 1 - FIRST STEPS
1 Introduction to human-in-the-loop machine learning
2 Getting started with human-in-the-loop machine learning
PART 2 - ACTIVE LEARNING
3 Uncertainty sampling
4 Diversity sampling
5 Advanced active learning
6 Applying active learning to different machine learning tasks
PART 3 - ANNOTATION
7 Working with the people annotating your data
8 Quality control for data annotation
9 Advanced data annotation and augmentation
10 Annotation quality for different machine learning tasks
PART 4 - HUMAN–COMPUTER INTERACTION FOR MACHINE LEARNING
11 Interfaces for data annotation
12 Human-in-the-loop machine learning products
424 pages, Paperback
Published July 20, 2021
22 people are currently reading
124 people want to read
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May 23, 2023Exceptional resource for anyone interested in understanding the dynamic relationship between humans and machine learning algorithms. It explores the vital role of human intervention in the iterative process of training and refining ML models. As you delve into the concepts presented in this book, check out this article on a similar topic https://business-review.eu/tech/data-.... It provides valuable insights into the critical role of data annotation in the realm of modern technology, including machine learning. By combining the knowledge gained from this book and the insights shared in the article, readers will develop a comprehensive understanding of the crucial relationship between human expertise and machine learning algorithms.
June 12, 2024
Excellent book, especially regarding the topic. There are many many books about machine learning, but at some point, they seem quite repetitive. This one is so far the only one I have seen that tackles the point of how to deal with the challenges of having humans in the centre and active learning in particular.
The practical aspect is also great because you can try all the concepts by running the code on your own, and if you are not sure about some details about some formulas about the human annotations, you can also look closely at the linked spreadsheets.
The only negative experience for me was that some parts felt difficult to read, not because of the difficult topic, but in terms of clarity of explanation.
The practical aspect is also great because you can try all the concepts by running the code on your own, and if you are not sure about some details about some formulas about the human annotations, you can also look closely at the linked spreadsheets.
The only negative experience for me was that some parts felt difficult to read, not because of the difficult topic, but in terms of clarity of explanation.
September 7, 2023
A good introductory course over Active Learning and the way that "clever" systems need to be augmented with humans. I read it as a soft introduction for Active Learning but was an easy, concise and very friendly read
Displaying 1 - 3 of 3 reviews