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: Fundamentals, Theory and Applications
  name:
    name part: Huang, Kaizhu.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Hussain, Amir.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Wang, Qiu-Feng.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Zhang, Rui.
    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: VII, 163 p. 66 illus., 46 illus. in color. online resource.
  abstract: The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.
  table of contents: Preface -- Introduction to Deep Density Models with Latent Variables -- Deep RNN Architecture: Design and Evaluation -- Deep Learning Based Handwritten Chinese Character and Text Recognition -- Deep Learning and Its Applications to Natural Language Processing -- Deep Learning for Natural Language Processing -- Oceanic Data Analysis with Deep Learning Models -- Index.
  note: edited by Kaizhu Huang, Amir Hussain, Qiu-Feng Wang, Rui Zhang.
  subject:
    topic: Medicine
  subject:
    topic: Artificial intelligence
  subject:
    topic: Algorithms
  subject:
    topic: Biomedicine, general
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Algorithms
  classification: R-RZ
  classification: 610
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Cognitive Computation Trends, 2
  identifier: 9783030060732
  identifier: https://doi.org/10.1007/978-3-030-06073-2
  record info:
    record creation date: 190215
    record change date: 20191220125833.0
    record identifier: 978-3-030-06073-2