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: Hands-On Image Generation with TensorFlow
  name:
    name part: Cheong, Soon
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Safari, an O'Reilly Media Company
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: xx
    date issued: 2020
    edition: 1st edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (306 pages)
  abstract: Implement various state-of-the-art architectures, such as GANs and autoencoders, for image generation using TensorFlow 2.x from scratch Key Features Understand the different architectures for image generation, including autoencoders and GANs Build models that can edit an image of your face, turn photos into paintings, and generate photorealistic images Discover how you can build deep neural networks with advanced TensorFlow 2.x features Book Description The emerging field of Generative Adversarial Networks (GANs) has made it possible to generate indistinguishable images from existing datasets. With this hands-on book, you'll not only develop image generation skills but also gain a solid understanding of the underlying principles. Starting with an introduction to the fundamentals of image generation using TensorFlow, this book covers Variational Autoencoders (VAEs) and GANs. You'll discover how to build models for different applications as you get to grips with performing face swaps using deepfakes, neural style transfer, image-to-image translation, turning simple images into photorealistic images, and much more. You'll also understand how and why to construct state-of-the-art deep neural networks using advanced techniques such as spectral normalization and self-attention layer before working with advanced models for face generation and editing. You'll also be introduced to photo restoration, text-to-image synthesis, video retargeting, and neural rendering. Throughout the book, you'll learn to implement models from scratch in TensorFlow 2.x, including PixelCNN, VAE, DCGAN, WGAN, pix2pix, CycleGAN, StyleGAN, GauGAN, and BigGAN. By the end of this book, you'll be well versed in TensorFlow and be able to implement image generative technologies confidently. What you will learn Train on face datasets and use them to explore latent spaces for editing new faces Get to grips with swapping faces with deepfakes Perform style transfer to convert a photo into a painting Build an…
  note: Cheong, Soon.
  note: Electronic resource.
  note: Electronic reproduction. Boston, MA : Safari, Available via World Wide Web. 2020.
  note: Mode of access: World Wide Web.
  note: Made available through: Safari, an O'Reilly Media Company.
  identifier: 9781838826789
  identifier: 9781838821104
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record creation date: 311220
    record change date: 20260129205146.1
    record identifier: in00004441244