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  • 1
    Online Resource
    Online Resource
    Getting started with deep learning (2018)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Artificial intelligence ; Neural networks (Computer science) ; Electronic books ; Electronic books ; local
    Abstract: Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning-especially deep neural networks-make a real difference in your organization? This lesson provides a practical overview of the foundations of neural networks and deep learning and also looks at deep networks. This lesson is for you because you are a data scientist or software developer who wants to understand and be able to use efficient tools to implement programs capable of learning from data.
    Note: "From Deep learning by Josh Patterson & Adam Gibson"--Cover. - Date of publication from resource description page. - Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 16, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492037330/?ar
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Kubeflow Operations Guide (2020)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (225 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: When deploying machine learning applications, building models is only a small part of the story. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads-a process Kubeflow makes much easier. With this practical guide, data scientists, data engineers, and platform architects will learn how to plan and execute a Kubeflow project that can support workflows from on-premises to the cloud. Kubeflow is an open source Kubernetes-native platform based on Google's internal machine learning pipelines, and yet major cloud vendors including AWS and Azure advocate the use of Kubernetes and Kubeflow to manage containers and machine learning infrastructure. In today's cloud-based world, this book is ideal for any team planning to build machine learning applications. With this book, you will: Get a concise overview of Kubernetes and Kubeflow Learn how to plan and build a Kubeflow installation Operate, monitor, and automate your installation Provide your Kubeflow installation with adequate security Serve machine learning models on Kubeflow
    Note: Online resource; Title from title page (viewed October 25, 2020)
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    Deep learning : a practitioner's approach (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Artificial intelligence ; Machine learning ; Neural networks (Computer science) ; Electronic books ; Electronic books ; local
    Abstract: Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning-especially deep neural networks-make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you'll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J's workflow tool Learn how to use DL4J natively on Spark and Hadoop
    Note: Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed August 7, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781491924570/?ar
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    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    Deep learning en action (2018)
    [Erscheinungsort nicht ermittelbar] : Editions First | Boston, MA : Safari
    Details
    Language: English , French
    Pages: 1 online resource (480 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: Plongez au coeur du Deep Learning Ce livre a été écrit pour tous ceux qui souhaitent s'initier au Deep Learning (apprentissage profond). Il est la suite logique du titre "Le Machine learning avec Python" paru en février 2018. Le Deep Learning est une technologie nouvelle qui évolue très rapidement. Ce livre en présente les bases principales de cette technologie. Au coeur de celle-ci on trouve les réseaux de neurones profonds, permettant de modéliser tous types de données et les réseaux de convolution, capables de traiter des images. Et enfin, cette technologie de plus en plus utilisée dans les applications d'intelligence artificielle introduit le notion de Reinforcement Learning (apprentissage par renforcement) qui permet d'optimiser les prises de décision par exemple pour le fonctionnement d'un robot. Au programme : La génèse du Deep Learning Les résaux de neuronnes Les bases des réseaux de type Deep learning L'architecture réseau Créer un réseau type Adapter le réseau à des besoins propres Les architectures spécifiques La vectorisation Le Deep Learning et DL4J sur Spark Au coeur de l'intelligence artificielle RL4J et Reinforcement Learning
    Note: Online resource; Title from title page (viewed May 25, 2018) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 5
    Online Resource
    Online Resource
    詳説 Deep Learning ―実務者のためのアプローチ (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Japan, Inc. | Boston, MA : Safari
    Details
    ISBN: 9784873118802
    Language: English , Japanese
    Pages: 1 online resource (608 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: エンタープライズ向けのディープラーニングの解説書。企業でディープラーニングアプリケーションを開発、運用するための実践的な手法を紹介します。対象読者はソフトウェア開発の現場で活躍する実務者。前半はディープラーニング初心者、後半はJavaエンジニア向けの構成です。機械学習、ニューラルネットワークの基礎から始め、ディープラーニングの基本的な概念、実際にチューニングを行う際のベストプラクティス、データのETL（抽出・変換・ロード）の方法、Apache Sparkを用いた並列化について、JavaライブラリDeep Learning4J（DL4J）の開発者でもある著者がわかりやすく丁寧に解説します。
    Note: Online resource; Title from title page (viewed August 8, 2019) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 6
    Online Resource
    Online Resource
    Shen du xue xi : nei hang ren de zuo fa = Deep learning : a practitioner's approach (2019)
    [Place of publication not identified] : GoTop Information, Inc.
    Details
    Title: 深度學習 : : 內行人的做法 = Deep learning : a practitioner's approach /
    ISBN: 9789865020262 , 9865020262
    Language: Chinese
    Pages: 1 online resource (576 pages) , illustrations
    Edition: [First edition].
    Uniform Title: Deep learning
    DDC: 006.3/1
    Keywords: Machine learning ; Neural networks (Computer science) ; Open source software ; Neural Networks, Computer ; Apprentissage automatique ; Réseaux neuronaux (Informatique) ; Logiciels libres ; Machine learning ; Neural networks (Computer science) ; Open source software ; Electronic books
    Abstract: Detailed summary in vernacular field.
    Note: Includes index
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
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