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 for data analytics
    sub title: foundations, biomedical applications, and challenges
  name:
    name part: Das, Himansu.
  name:
    name part: Pradhan, Chittaranjan.
  name:
    name part: Dey, Nilanjan
    name part: 1984-
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2020
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis.--
  note: edited by Himansu Das, Chittaranjan Pradhan and Nilanjan Dey.
  note: Includes bibliographical references and index.
  subject:
    topic: Machine learning
  subject:
    topic: Apprentissage automatique
  subject:
    topic: Machine learning
  classification: Q325.5
  classification: 006.3/1
  related item:
    identifier: (OCoLC)1130899746
  identifier: 9780128226087
  identifier: 0128226080
  identifier: 9780128197646
  identifier: 0128197641
  identifier: 9780128226087 Ingram Content Group
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: YDX
    record creation date: 200604
    record change date: 20260327164625.5
    record identifier: in00005766529
    language of cataloging:
      language term: eng