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Practical Weak Supervision /
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, Se...
| Autores principales: | , , |
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| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
O'Reilly Media, Inc.,
2021.
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| Edición: | 1st edition. |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | 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. |
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| Descripción Física: | 1 online resource (200 pages) |