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Slideshare: Combining Human and Machine Intelligence
Where do People Fit Best into the Machine Learning Loop?
🎙️ Robert Munro interviewed
🎙️ Design for Machine Learning
10 Ways that Human-in-the-Loop Machine Learning is Used Today
Active Transfer Learning with PyTorch
🎙️ Interview with Robert Munro
🎙️ 1/5 Human-in-the-Loop Machine Learning (Robert Munro Monarch)
🎙️ 2/5 Human-in-the-Loop Machine Learning (Robert Munro Monarch)
🎙️ 3/5 Human-in-the-Loop Machine Learning (Robert Munro Monarch)
🎙️ 4/5 Human-in-the-Loop Machine Learning (Robert Munro Monarch)
🎙️ 5/5 Human-in-the-Loop Machine Learning (Robert Munro Monarch)
🎙️ A Chat with Human in the Loop Expert and Author, Robert Monarch
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An excellent reference for learning about active learning from both practical and theoretical perspectives.
Look inside
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.
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.
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This book is a must-read for all practitioners who want to integrate active learning in their development cycles. Well written, good coverage, and filled with practical examples.
Very apt real-world examples. I will recommend this book to my colleagues.
The best reference about supervised machine learning from a real expert and practitioner.
