02096nam\a2200301\a\4500 57891 20260707121813.0 260707t20192019nyua b 001 0 eng d 9781617295560 YDX eng YDX rda OCLCQ TFW OCLCO BDX JRZ BD-DhIUB 006.31 23 L285g Langr, Jakub author. GANs in action : deep learning with generative adversarial networks / Jakub Langr, Vladimir Bok. Generative Adversarial Networks in action 1st ed. New York: Manning Publications Co. 2019 xxiii, 214 pages : illustrations ; 24 cm Includes bibliographical references and index. Introduction to GANs and generative modeling -- Advanced topics in GANs -- Where to go from here. 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. CSE ps REF Omni concept Machine learning Technological innovations. Artificial intelligence Computer programs. Bok, Vladimir author. ddc BK 57891 58065 0 0 ddc 0 1 IUBL IUBL REF 2026-06-23 PURCHASE 5683.00 2019 0 006.31 L285g 029870 2026-07-07 12:21:07 01 11366.00 2026-06-23 BK 0 0 ddc 0 0 IUBL IUBL GEN 2026-06-23 PURCHASE 5683.00 2019 0 006.31 L285g 029871 2026-07-07 12:21:00 02 11366.00 2026-06-23 BK