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: Handbook of Deep Learning Applications
  name:
    name part: Balas, Valentina Emilia.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Roy, Sanjiban Sekhar.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Sharma, Dharmendra.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Samui, Pijush.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    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: VI, 383 p. 181 illus., 127 illus. in color. online resource.
  abstract: This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain-computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.
  table of contents: Designing a Neural Network from scratch for Big Data powered by Multi-node GPUs -- Deep Learning for Scene Understanding -- Deep Learning for Driverless Vehicles -- Deep Learning for Document Representation -- Deep learning for marine species recognition -- Deep molecular representation in Cheminformatics -- Deep Learning in eHealth -- Deep Learning for Brain Computer Interfaces -- Deep Learning in Gene Expression Modeling.
  note: edited by Valentina Emilia Balas, Sanjiban Sekhar Roy, Dharmendra Sharma, Pijush Samui.
  subject:
    topic: Computational intelligence
  subject:
    topic: Artificial intelligence
  subject:
    topic: Signal processing
  subject:
    topic: Image processing
  subject:
    topic: Speech processing systems
  subject:
    topic: Neural networks (Computer science)
  subject:
    topic: Data mining
  subject:
    topic: Computational Intelligence
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Signal, Image and Speech Processing
  subject:
    topic: Mathematical Models of Cognitive Processes and Neural Networks
  subject:
    topic: Data Mining and Knowledge Discovery
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Smart Innovation, Systems and Technologies, 136
  identifier: 9783030114794
  identifier: https://doi.org/10.1007/978-3-030-11479-4
  record info:
    record creation date: 190225
    record change date: 20191220130548.0
    record identifier: 978-3-030-11479-4