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: Trends in deep learning methodologies
    sub title: algorithms, applications, and systems
  name:
    name part: Piuri, Vincenzo
    role:
      role term: editor.
  name:
    name part: Raj, Sandeep
    role:
      role term: editor.
  name:
    name part: Genovese, Angelo
    name part: 1985-
    role:
      role term: editor.
  name:
    name part: Srivastava, Rajshree
    role:
      role term: editor.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2021
    copyright date: 2021
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xvii, 288 pages) : illustrations.
  abstract: Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models.
  note: edited by Vincenzo Piuri, Sandeep Raj, Angelo Genovese, Rajshree Srivastava.
  note: Includes bibliographical references and index.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Neural networks (Computer science)
  subject:
    topic: Intelligence artificielle
  subject:
    topic: Réseaux neuronaux (Informatique)
  subject:
    topic: artificial intelligence
  subject:
    topic: Artificial intelligence
  subject:
    topic: Neural networks (Computer science)
  classification: Q335 .T74 2021
  classification: 006.3
  related item:
    title info:
      title: Hybrid computational intelligence for pattern analysis and understanding
  identifier: 0128232684
  identifier: 9780128222263
  identifier: 0128222263
  identifier: 9780128232682
  identifier: 9780128232682 Ingram Content Group
  identifier: 9780128232682 O'Reilly Media
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: UKMGB
    record creation date: 201109
    record change date: 20260327165832.8
    record identifier: in00005767725
    language of cataloging:
      language term: eng