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  • 1
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Language: English
    Pages: 1 online resource (250 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn the tools, best practices, and challenges involved in building a real-world ML application step-by-step. Author Emmanuel Ameisen, who worked as a data scientist at Zipcar and led Insight Data Science’s AI program, demonstrates key ML concepts with code snippets, illustrations, and screenshots from the book’s example application. The first part of this guide shows you how to plan and measure success for an ML application. Part II shows you how to build a working ML model, and Part III explains how to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies. This book will help you: Determine your product goal and set up a machine learning problem Build your first end-to-end pipeline quickly and acquire an initial dataset Train and evaluate your ML model and address performance bottlenecks Deploy and monitor models in a production environment
    Note: Online resource; Title from title page (viewed February 25, 2020)
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  • 2
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    ISBN: 9788328371866
    Language: English , Polish
    Pages: 1 online resource (224 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: To książka przeznaczona dla programistów i menedżerów, którzy wśród rodzących się idei uczenia maszynowego wciąż poszukują rozwiązań dla swojego biznesu. Autor omawia krok po kroku proces tworzenia i wdrażania aplikacji opartej na uczeniu maszynowym, a praktyczne koncepcje przedstawia za pomocą przykładowych kodów, rysunków i wywiadów z liderami w tej dziedzinie. Podpowiada, jak planować aplikację i oceniać jej jakość. Wyjaśnia także, jak budować skuteczny model, i demonstruje metody jego systematycznego usprawniania, aż do momentu osiągnięcia celu. W końcowej części opisuje strategie wdrażania i monitorowania modelu. W odróżnieniu od innych pozycji poświęconych uczeniu maszynowym ten przewodnik skupia się przede wszystkim na definiowaniu problemów, diagnozowaniu modeli i ich wdrażaniu.
    Note: Online resource; Title from title page (viewed January 20, 2021) , Mode of access: World Wide Web.
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  • 3
    Orig.schr. Ausgabe: 初版.
    Title: 機械学習による実用アプリケーション構築 : : 事例を通じて学ぶ, 設計から本番稼働までのプロセス /
    Publisher: オライリー・ジャパン,
    ISBN: 9784873119502 , 4873119502
    Language: Japanese
    Pages: 1 online resource (256 pages)
    Edition: Shohan.
    Uniform Title: Building machine learning powered applications
    DDC: 006.31
    Keywords: Machine learning ; Application software Development ; Apprentissage automatique ; Logiciels d'application ; Développement ; Application software ; Development ; Machine learning ; Electronic books
    Abstract: "Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers--including experienced practitioners and novices alike--will learn the tools, best practices, and challenges involved in building a real-world ML application step by step. Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies. This book will help you:Define your product goal and set up a machine learning problemBuild your first end-to-end pipeline quickly and acquire an initial datasetTrain and evaluate your ML models and address performance bottlenecksDeploy and monitor your models in a production environment." --
    Note: Online resource; title from title details screen (O'Reilly, viewed April 19, 2022)
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