Weakly Supervised Learning: Doing More with Less Data

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.

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.

• Get up to speed on the field of weak supervision, including ways to use it as part of the data science process
• Use Snorkel AI for weak supervision and data programming
• Get code examples for using Snorkel to label text and image datasets
• Use a weakly labeled dataset for text and image classification
• Learn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling

Russell Jurney runs a consultancy, Data Syndrome, that helps companies develop their own lead generation systems and other analytics products. He cut his data teeth in casino gaming, building web apps to analyze the performance of slot machines in the U.S. and Mexico. After dabbling in entrepreneurship, interactive media and journalism, he moved to Silicon Valley to build analytics applications at scale at Ning and LinkedIn. He lives in Pacifica, CA, with Bella the Data Dog. You can reach him at rjurney@datasyndrome.com.