rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Deep Learning: Convergence to Big Data Analytics
  name:
    name part: Khan, Murad.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Jan, Bilal.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Farman, Haleem.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: si
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 79 p. 27 illus., 18 illus. in color. online resource.
  abstract: This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.
  table of contents: Chapter 1. Introduction -- Chapter 2. Big Data Analytics -- Chapter 3. Deep Learning Methods and Applications -- Chapter 4. Integration of Big Data and Deep Learning -- Chapter 5. Future Aspects. .
  note: by Murad Khan, Bilal Jan, Haleem Farman.
  subject:
    topic: Database management
  subject:
    topic: Artificial intelligence
  subject:
    topic: Data structures (Computer science)
  subject:
    topic: Big data
  subject:
    topic: Database Management
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Data Structures
  subject:
    topic: Big Data
  classification: QA76.9.D3
  classification: 005.74
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Computer Science
  identifier: 9789811334597
  identifier: https://doi.org/10.1007/978-981-13-3459-7
  record info:
    record creation date: 181230
    record change date: 20191027051640.0
    record identifier: 978-981-13-3459-7