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.
Wee Hyong 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.
EUR 17,25 für den Versand von USA nach Deutschland
Versandziele, Kosten & DauerEUR 0,60 für den Versand von USA nach Deutschland
Versandziele, Kosten & DauerAnbieter: PBShop.store US, Wood Dale, IL, USA
PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000. Bestandsnummer des Verkäufers WO-9781492077060
Anzahl: 3 verfügbar
Anbieter: moluna, Greven, Deutschland
Kartoniert / Broschiert. 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. Bestandsnummer des Verkäufers 360342895
Anzahl: 3 verfügbar
Anbieter: BargainBookStores, Grand Rapids, MI, USA
Paperback or Softback. Zustand: New. Practical Weak Supervision: Doing More with Less Data 0.69. Book. Bestandsnummer des Verkäufers BBS-9781492077060
Anzahl: 5 verfügbar
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich
PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000. Bestandsnummer des Verkäufers WO-9781492077060
Anzahl: 3 verfügbar
Anbieter: THE SAINT BOOKSTORE, Southport, Vereinigtes Königreich
Paperback / softback. Zustand: New. New copy - Usually dispatched within 4 working days. 209. Bestandsnummer des Verkäufers B9781492077060
Anzahl: 3 verfügbar
Anbieter: GreatBookPrices, Columbia, MD, USA
Zustand: New. Bestandsnummer des Verkäufers 41173743-n
Anzahl: 3 verfügbar
Anbieter: California Books, Miami, FL, USA
Zustand: New. Bestandsnummer des Verkäufers I-9781492077060
Anzahl: Mehr als 20 verfügbar
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Neuware - Getting quality labeled data for supervised learning is an important step towards training performant machine learning models. In many real-world projects, getting labeled data often takes up significant amount of time. Weak Supervision is emerging as an important catalyst towards enabling data science team to fuse insights from labeling functions in order to produce weakly labeled datasets that can be used as inputs for machine learning and deep learning tasks. Bestandsnummer des Verkäufers 9781492077060
Anzahl: 2 verfügbar
Anbieter: GreatBookPrices, Columbia, MD, USA
Zustand: As New. Unread book in perfect condition. Bestandsnummer des Verkäufers 41173743
Anzahl: 3 verfügbar
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
Zustand: New. Bestandsnummer des Verkäufers 41173743-n
Anzahl: 3 verfügbar