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.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
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.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
Anbieter: PBShop.store US, Wood Dale, IL, USA
PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000. Artikel-Nr. WO-9781492077060
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich
PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000. Artikel-Nr. WO-9781492077060
Anzahl: 2 verfügbar
Anbieter: California Books, Miami, FL, USA
Zustand: New. Artikel-Nr. I-9781492077060
Anzahl: Mehr als 20 verfügbar
Anbieter: Rarewaves.com USA, London, LONDO, Vereinigtes Königreich
Paperback. Zustand: New. 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 processUse Snorkel AI for weak supervision and data programmingGet code examples for using Snorkel to label text and image datasetsUse a weakly labeled dataset for text and image classificationLearn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling. Artikel-Nr. LU-9781492077060
Anzahl: 1 verfügbar
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
Zustand: New. Artikel-Nr. 41173743-n
Anzahl: Mehr als 20 verfügbar
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
Zustand: As New. Unread book in perfect condition. Artikel-Nr. 41173743
Anzahl: Mehr als 20 verfügbar
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
Paperback. Zustand: Brand New. 200 pages. 9.19x7.00x0.41 inches. In Stock. Artikel-Nr. x-1492077062
Anzahl: 2 verfügbar
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Zustand: New. In English. Artikel-Nr. ria9781492077060_new
Anzahl: 2 verfügbar
Anbieter: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irland
Zustand: New. 2021. Paperback. . . . . . Artikel-Nr. V9781492077060
Anzahl: 1 verfügbar
Anbieter: moluna, Greven, Deutschland
Zustand: New. 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. There s a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Se. Artikel-Nr. 360342895
Anzahl: Mehr als 20 verfügbar