Book Review: Human-in-the-Loop Machine Learning: Active Learning and Annotation for Human-Centered AI
The book “Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI” is written by Robert (Munro) Monarch. It was published 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, concentrate nearly entirely on the algorithms, rather than on the human-computer interface aspect of the systems. This might result in a significant knowledge gap for data scientists working in real-world 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 whole machine learning process, including strategies for annotation, active learning, transfer learning, and employing machine learning to optimize every stage of the process.
Human-in-the-Loop Machine Learning describes how people and machines can collaborate successfully. Best practices for choosing sample data for human feedback, quality control for human annotations, and creating annotation interfaces may all be found here. You’ll learn how to make training data for labeling, object recognition, and semantic segmentation, as well as sequence labeling. The book begins with the fundamentals and continues to more complex topics such as transfer learning and self-supervision in annotation workflows. With the help of this book, you’ll be able to discover and manage individuals who can annotate data, choose annotation quality control techniques, and develop interfaces that enhance accuracy and efficiency.
Robert (Munro) Monarch is a machine learning data scientist and engineer who has worked for businesses including Apple, Amazon, Google, and IBM. Robert has a Ph.D. 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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