Practical Weak Supervision - by Wee Hyong Tok & Amit Bahree & Senja Filipi (Paperback)
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<p/><br></br><p><b> Book Synopsis </b></p></br></br><p>Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models.</p><p>You'll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies have pursued ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build.</p><ul><li>Get up to speed on the field of weak supervision, including ways to use it as part of the data science process</li><li>Use Snorkel AI for weak supervision and data programming</li><li>Get code examples for using Snorkel to label text and image datasets</li><li>Use a weakly labeled dataset for text and image classification</li><li>Learn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling</li></ul><p/><br></br><p><b> About the Author </b></p></br></br><p>is a product and AI leader with a background in product management, machine learning/deep learning, research, and working on complex technical engagements with customers. Over the years, he has demonstrated that the early thought-leadership whitepapers he wrote on tech trends have become reality, and are deeply integrated into many products. Wee Hyong has worn many hats in his career--developer, program/product manager, data scientist, researcher, and strategist, and his range of experience has given him unique superpowers to lead and define the strategy for high-performing data and AI innovation teams.</p>
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