deep learning with generative adversarial networks Langr, Jakub creator author. Bok, Vladimir author. text bibliography nyu New York Manning Publications Co. 2019 2019 1st ed. monographic eng

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xxiii, 214 pages : illustrations ; 24 cm Generative Adversarial Networks, GANs, are an incredible AI technology capable of creating images, sound, and videos that are indistinguishable from the "real thing". By pitting two neural networks against each other, one to generate fakes and one to spot them, GANs rapidly learn to produce photo-realistic faces and other media objects. With the potential to produce stunningly realistic animations or shocking deepfakes, GANs are a huge step forward in deep learning systems. "GANs in action" teaches you to build and train your own Generative Adversarial Networks. You'll start by creating simple generator and discriminator networks that are the foundation of GAN architecture. Then, following numerous hands-on examples, you'll train GANs to generate high-resolution images, image-to-image translation, and targeted data generation. Along the way, you'll find pro tips for making your system smart, effective, and fast. Introduction to GANs and generative modeling -- Advanced topics in GANs -- Where to go from here. Jakub Langr, Vladimir Bok. Includes bibliographical references and index. CSE ps REF Omni concept Machine learning Technological innovations Artificial intelligence Computer programs 006.31 L285g 9781617295560 YDX 260707 20260707121813.0 57891 eng