Human-In-The-Loop Machine Learning - by Monarch (Paperback)
Similar Products
Products of same category from the store
All
Product info
<p/><br></br><p><b> Book Synopsis </b></p></br></br><b><i>Human-in-the-Loop Machine Learning</i> lays out methods for humans and machines to work together effectively.</b> <p/><b>Summary</b><br> 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. <i>Human-in-the-Loop Machine Learning</i> 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. <p/> Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. <p/> <b>About the technology</b><br> 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. <p/> <b>About the book</b><br> <i>Human-in-the-Loop Machine Learning</i> 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. <p/> <b>What's inside</b> <p/> Identifying the right training and evaluation data<br> Finding and managing people to annotate data<br> Selecting annotation quality control strategies<br> Designing interfaces to improve accuracy and efficiency <p/> <b>About the author</b><br> <b>Robert (Munro) Monarch</b> 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. <p/>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. <p/><b>Table of Contents</b> <p/>PART 1 - FIRST STEPS<br> 1 Introduction to human-in-the-loop machine learning<br> 2 Getting started with human-in-the-loop machine learning<br> PART 2 - ACTIVE LEARNING<br> 3 Uncertainty sampling<br> 4 Diversity sampling<br> 5 Advanced active learning<br> 6 Applying active learning to different machine learning tasks<br> PART 3 - ANNOTATION<br> 7 Working with the people annotating your data<br> 8 Quality control for data annotation<br> 9 Advanced data annotation and augmentation<br> 10 Annotation quality for different machine learning tasks<br> PART 4 - HUMAN-COMPUTER INTERACTION FOR MACHINE LEARNING<br> 11 Interfaces for data annotation<br> 12 Human-in-the-loop machine learning products<p/><br></br><p><b> About the Author </b></p></br></br><b>Robert (Munro) Monarch</b> 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. <p/> 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.
Price History
Cheapest price in the interval: 49.49 on October 28, 2021
Most expensive price in the interval: 49.49 on December 20, 2021
Price Archive shows prices from various stores, lets you see history and find the cheapest. There is no actual sale on the website. For all support, inquiry and suggestion messagescommunication@pricearchive.us