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 computational imaging
  name:
    name part: Heckel, Reinhard
    role:
      role term: creator
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2025
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource : illustrations (some color)
  abstract: This textbook offers an introduction to deep learning for solving inverse problems. It introduces deep neural networks and deep neural network based signal and image reconstruction techniques. It discusses robustness aspects, how to evaluate and test different methods, and data-centric aspects.
  target audience: Specialized.
  target audience: specialized
  note: Reinhard Heckel.
  note: Includes bibliographical references and index.
  subject:
    topic: Image reconstruction
  subject:
    topic: Image reconstruction
    topic: Mathematical models
  subject:
    topic: Deep learning (Machine learning)
  subject:
    topic: Reconstruction d'image
  subject:
    topic: Reconstruction d'image
    topic: Modèles mathématiques
  subject:
    topic: Apprentissage profond
  subject:
    topic: MATHEMATICS
  subject:
    topic: Discrete Mathematics
  subject:
    topic: Electronics
  subject:
    topic: TECHNOLOGY & ENGINEERING
  subject:
    topic: Graphical & digital media applications
  subject:
    topic: Computers and IT
  classification: TA1637 .H43 2025
  classification: 006.4/2
  identifier: 9780198947202
  identifier: 0198947208
  identifier: 0198947194
  identifier: 9780198947196
  identifier: 9780198947172
  identifier: 9780198947189
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: STBDS
    record creation date: 250214
    record change date: 20260319205729.4
    record identifier: in00005728011
    language of cataloging:
      language term: eng