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: Computer engineering machine learning and neural networks
    sub title: a computer engineering perspective
  name:
    name part: Chen, Yiran
    name part: 1976-
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Li, Hai
    name part: 1975-
    role:
      role term: author.
  name:
    name part: Yang, Huanrui
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2026
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xxiv, 312 pages) : illustrations (chiefly color)
  abstract: This is the first textbook focusing on practicality of machine learning (ML) and deep neural networks (DNN), by introducing methods that enable engineering applications of ML and DNN models. The authors describe many methodologies that are widely used in designing, training, and deploying of these models and discuss their applicability under various contexts. Coverage begins with the basic knowledge of machine learning and deep neural networks and their applications in solving practical engineering problems. It then proceeds through a series of computer engineering methods commonly used in developing machine learning and deep neural network models. The book also explains how to improve the training and inference performance in terms of model accuracy, size, runtime, etc. by considering various requirements and availability of data in the applications. Techniques that are widely adopted in both industry and academia are discussed. Tutorials and projects designed to practice the introduced techniques are provided using popular development frameworks of machine learning. Emphasizes practice over theoretical foundations, making content accessible to engineering students and engineers; Includes in-depth discussion of popular DNN models and their applications; Discusses engineering methods and tricks widely adopted in practice for using ML and DNN to solve engineering problems. .
  table of contents: Introduction -- Perceptron and back propagation -- Image feature and 2d convolution -- Convolutional neural network -- Efficient neural architecture -- Recurrent neural network and language models -- Transfer learning -- Generative adversarial network gan -- Automl and neural architecture search -- Distributed computing and federated learning -- Summary.
  note: Yiran Chen, Hai Li, Huanrui Yang.
  note: Includes bibliographical references.
  subject:
    topic: Machine learning
  subject:
    topic: Neural networks (Computer science)
  subject:
    topic: Computer engineering
  classification: Q325.5 .C44 2026
  classification: 006.3/1
  related item:
    title info:
      title: Computer Engineering Machine Learning and Neural Networks
    name:
      name part: Chen, Yiran
    origin info:
      publisher: Cham : Springer,c2026
  related item:
    title info:
      title: Engineering Series
  identifier: 9783032209795 (electronic bk.)
  identifier: 303220979X
  identifier: 9783032209788
  identifier: 9783032209801
  identifier: 9783032209818
  location:
    physical location: POD
  record info:
    record content source: GW5XE
    record creation date: 260618
    record change date: 20260726034402.1
    record identifier: in00005789119
    language of cataloging:
      language term: eng