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 Belief Nets in C++ and CUDA C: Volume 1
    sub title: Restricted Boltzmann Machines and Supervised Feedforward Networks
  name:
    name part: Masters, Timothy.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: IX, 219 p. 33 illus., 20 illus. in color. online resource.
  abstract: Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. The first of three in a series on C++ and CUDA C deep learning and belief nets, Deep Belief Nets in C++ and CUDA C: Volume 1 shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a thought process that is capable of learning abstract concepts built from simpler primitives. As such, you'll see that a typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. All the routines and algorithms presented in the book are available in the code download, which also contains some libraries of related routines. You will: Employ deep learning using C++ and CUDA C Work with supervised feedforward networks Implement restricted Boltzmann machines Use generative samplings Discover why these are important.
  table of contents: 1. Introduction -- 2. Supervised Feedforward Networks -- 3. Restricted Boltzmann Machines -- 4. Greedy Training: Generative Samplings -- 5. DEEP Operating Manual.
  note: by Timothy Masters.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Programming languages (Electronic computers)
  subject:
    topic: Big data
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Programming Languages, Compilers, Interpreters
  subject:
    topic: Big Data
  subject:
    topic: Big Data/Analytics
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781484235911
  identifier: https://doi.org/10.1007/978-1-4842-3591-1
  record info:
    record creation date: 180423
    record change date: 20191220130536.0
    record identifier: 978-1-4842-3591-1