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: Applied deep learning with TensorFlow 2
    sub title: learn to implement advanced deep learning techniques with Python
  name:
    name part: Michelucci, Umberto
    role:
      role term: creator
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  genre: elektronické knihy
  genre: electronic books
  origin info:
    place:
      place term: nyu
    date issued: 2022
    edition: 2nd ed.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xxviii, 380 pages : illustrations)
  abstract: Understand how neural networks work and learn how to implement them using TensorFlow 2.0 and Keras. This new edition focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This book is designed so that you can focus on the parts you are interested in. You will explore topics as regularization, optimizers, optimization, metric analysis, and hyper-parameter tuning. In addition, you will learn the fundamentals ideas behind autoencoders and generative adversarial networks. All the code presented in the book will be available in the form of Jupyter notebooks which would allow you to try out all examples and extend them in interesting ways. A companion online book is available with the complete code for all examples discussed in the book and additional material more related to TensorFlow and Keras. All the code will be available in Jupyter notebook format and can be opened directly in Google Colab (no need to install anything locally) or downloaded on your own machine and tested locally.
  table of contents: Optimization and Neural Networks -- Hands-on with a single neuron -- Feed-Forward Neural Networks -- Regularization -- Advanced optimizers -- Hyper-parameter tuning -- Convolutional neural networks -- A Brief Introduction to Recurrent Neural Networks -- Autoencoders -- Metric analysis -- Generative Adversarial Networks (GANs) -- Appendix A: Introduction to Keras -- Appendix B: customizing Keras -- Index.
  note: Umberto Michelucci.
  note: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  note: Includes bibliographical references and index.
  subject:
    topic: Python (Computer program language)
  subject:
    topic: Machine learning
  subject:
    topic: Neural networks (Computer science)
  related item:
    title info:
      title: ITpro collection
  related item:
    identifier: (OCoLC)1289363782
  identifier: 9781484280201
  identifier: 1484280202
  identifier: 9781484280195
  identifier: 1484280199
  identifier: https://proxy.k.utb.cz/login
  access condition: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  record info:
    record content source: ORMDA
    record creation date: 220405
    record change date: 20240717213016.0
    record identifier: kn-on1308983931
    language of cataloging:
      language term: eng