Practical Weak Supervision
Doing More with Less Data
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Getting quality labeled data for supervised learning is an important step towards training performant machine learning models. In many real-world projects, getting labeled data often takes up significant amount of time. Weak Supervision is emerging as an important catalyst towards enabling data science team to fuse insights from labeling functions in order to produce weakly labeled datasets that can be used as inputs for machine learning and deep learning tasks. In this book, authors Amit Bahree, Senja Filipi, and Wee Hyong Tok from Microsoft help those working on machine learning projects fac...
Getting quality labeled data for supervised learning is an important step towards training performant machine learning models. In many real-world projects, getting labeled data often takes up significant amount of time. Weak Supervision is emerging as an important catalyst towards enabling data science team to fuse insights from labeling functions in order to produce weakly labeled datasets that can be used as inputs for machine learning and deep learning tasks. In this book, authors Amit Bahree, Senja Filipi, and Wee Hyong Tok from Microsoft help those working on machine learning projects face the typical challenges of getting good, quality labeled data for their projects. Readers will learn: * The lifecycle of enterprise machine learning projects, and the emerging area of weak supervision * How to use Snorkel for data labeling * How to use the weakly labeled dataset for Natural Language Processing * How to use the weakly labeled dataset for Computer Vision