Practical Weak Supervision Doing More with Less Data
Most data scientists and engineers today rely on quality labeled data to train their machine learning models. But building training sets manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Amit Bahree, Senja Filipi, and Wee Hyong Tok from Microsoft 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 pursue 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 a practical overview of weak supervision
- Dive into data programming with help from Snorkel
- Perform text classification using Snorkel's weakly labeled dataset
- Use Snorkel's labeled indoor-outdoor dataset for computer vision tasks
- Scale up weak supervision using scaling strategies and underlying technologies
| Publisher Name | OReilly Media |
|---|---|
| Author Name | Hagendorf, Col |
| Format | Audio |
| Bisac Subject Major | COM |
| Language | NG |
| Isbn 10 | 1492077062 |
| Isbn 13 | 9781492077060 |
| Target Age Group | min:NA, max:NA |
| Dimensions | 00.00" H x 00.00" L x 00.00" W |
| Page Count | 200 |
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.
