Deep Learning for the Internet of Things.

How can the advantages of deep learning be brought to the emerging world of embedded IoT devices? The authors discuss several core challenges in embedded and mobile deep learning, as well as recent solutions demonstrating the feasibility of building IoT applications that are powered by effective, ef...

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Publicado en:Computer (00189162) Vol. 51; no. 5; pp. 32 - 42
Autores principales: Yao, Shuochao, Zhao, Yiran, Zhang, Aston, Hu, Shaohan, Shao, Huajie, Zhang, Chao, Su, Lu, Abdelzaher, Tarek
Formato: Artículo
Publicado: IEEE May2018
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Deep Learning for the Internet of Things.
      aug:
        au:
          Yao, Shuochao
          Zhao, Yiran
          Zhang, Aston
          Hu, Shaohan
          Shao, Huajie
          Zhang, Chao
          Su, Lu
          Abdelzaher, Tarek
        affil:
          University of Illinois Urbana-Champaign (UIUC)
          Amazon AI
          IBM Thomas J. Watson Research Center
          UIUC
          State University of New York, Buffalo
      su:
        Deep learning
        Internet of things
        Computer vision
        Problem solving
        Application software
      sug:
        subj:
          Deep learning
          Internet of things
          Computer vision
          Problem solving
          Application software
      keyword:
        deep learning
        embedded learning
        Internet of Things
        IoT
        machine learning
        mobile and embedded deep learning
        neural networks
      ab: How can the advantages of deep learning be brought to the emerging world of embedded IoT devices? The authors discuss several core challenges in embedded and mobile deep learning, as well as recent solutions demonstrating the feasibility of building IoT applications that are powered by effective, efficient, and reliable deep learning models.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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