Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Online Resource
    Online Resource
    Predictive maintenance meets predictive analytics : gathering and analyzing IoT data for manufacturing (2016)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (50 min., 58 sec.)) , digital, sound, color
    Keywords: Machinery ; Maintenance and repair ; Information technology ; Management ; Computer networks ; Maintenance and repair ; Internet of things ; Cloud computing ; Electronic videos ; local
    Abstract: "In a talk aimed at data scientists, students, researchers, and nontechnical professionals, Danielle Dean introduces the landscape and challenges of predictive maintenance applications in the manufacturing industry. Predictive maintenance, a technique to predict when an in-service machine will fail so that maintenance can be planned in advance, encompasses failure prediction, failure diagnosis, failure type classification, and recommendation of maintenance actions after failure. Danielle reviews predictive maintenance problems from the perspectives of both the traditional, reliability-centered maintenance field and IoT applications, discussing problem coverage, applicable predictive models based on data available, and what data must be collected to perform predictive maintenance tasks. You'll learn how to bridge the data-driven approach and the problem-driven approach by articulating what types of data are needed for different predictive maintenance applications."--Resource description page.
    Note: Title from title screen (viewed September 1, 2016)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Deep learning with Azure : building and deploying artificial intelligence solutions on the Microsoft AI platform (2018)
    [Place of publication not identified] : Apress
    Details
    ISBN: 9781484236796
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Microsoft Azure (Computing platform) ; Artificial intelligence ; Machine learning ; Electronic books ; local ; Electronic books
    Abstract: Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure Who This Book Is For Professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful.
    Note: Includes bibliographical references. - Description based on online resource; title from cover (Safari, viewed September 14, 2018)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...