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...
Gespeichert in:
| Beteiligte Personen: | , , |
|---|---|
| Körperschaft: | |
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
[Erscheinungsort nicht ermittelbar]
O'Reilly Media, Inc.
2021
|
| Ausgabe: | 1st edition. |
| Links: | https://learning.oreilly.com/library/view/-/9781492077053/?ar |
| Zusammenfassung: | 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. |
| Beschreibung: | Online resource; Title from title page (viewed November 25, 2021) |
| Umfang: | 1 Online-Ressource (200 Seiten) |
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| spelling | Tok, Wee VerfasserIn aut Practical Weak Supervision Tok, Wee 1st edition. [Erscheinungsort nicht ermittelbar] O'Reilly Media, Inc. 2021 1 Online-Ressource (200 Seiten) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Online resource; Title from title page (viewed November 25, 2021) 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. Bahree, Amit VerfasserIn aut Filipi, Senja VerfasserIn aut Safari, an O'Reilly Media Company. MitwirkendeR ctb |
| spellingShingle | Tok, Wee Bahree, Amit Filipi, Senja Practical Weak Supervision |
| title | Practical Weak Supervision |
| title_auth | Practical Weak Supervision |
| title_exact_search | Practical Weak Supervision |
| title_full | Practical Weak Supervision Tok, Wee |
| title_fullStr | Practical Weak Supervision Tok, Wee |
| title_full_unstemmed | Practical Weak Supervision Tok, Wee |
| title_short | Practical Weak Supervision |
| title_sort | practical weak supervision |
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