Book Review: Human Machine Learning in the Loop: Active Learning and Annotation for Human-Centered AI
by Sumana Bhattacharya
October 12, 2021
The book “Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI” is written by Robert (Munro) Monarch. It was released on July 20, 2021. The majority of machine learning systems in use today learn through human feedback. Most machine learning courses, on the other hand, focus almost entirely on algorithms, rather than the human-computer interface aspect of systems. This could lead to a significant knowledge gap for data scientists working in the real world of machine learning, where they spend more time managing data than developing algorithms. Human-in-the-Loop Machine Learning is a practical guide to improving the entire machine learning process, including strategies for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process.
Human machine learning in the loop describes how people and machines can collaborate successfully. Best practices for choosing sample data for human feedback, quality control of human annotations, and creating annotation interfaces can all be found here. You will learn how to create training data for labeling, object recognition and semantic segmentation, as well as sequence labeling. The book starts with the fundamentals and moves on to more complex topics such as transfer learning and self-supervision in annotation workflows. With the help of this book, you will be able to discover and manage people who can annotate data, choose annotation quality control techniques, and develop interfaces that improve accuracy and efficiency. .
Robert (Munro) Monarch is a machine learning data scientist and engineer who has worked for companies including Apple, Amazon, Google, and IBM. Robert has a doctorate. from Stanford in Human-in-the-Loop Machine Learning for Healthcare and Disaster Response and is also a Disaster Response Practitioner.
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