LEADER 03127nam 22005775 450 001 996546829703316 005 20230704115504.0 010 $a3-031-32661-X 024 7 $a10.1007/978-3-031-32661-5 035 $a(CKB)27451969000041 035 $a(MiAaPQ)EBC30618377 035 $a(Au-PeEL)EBL30618377 035 $a(DE-He213)978-3-031-32661-5 035 $a(PPN)272252050 035 $a(EXLCZ)9927451969000041 100 $a20230704d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA Primer on Generative Adversarial Networks$b[electronic resource] /$fby Sanaa Kaddoura 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (91 pages) 225 1 $aSpringerBriefs in Computer Science,$x2191-5776 311 $a9783031326608 327 $aOverview of GAN Structure -- Your First GAN -- Real World Applications -- Conclusion. 330 $aThis book is meant for readers who want to understand GANs without the need for a strong mathematical background. Moreover, it covers the practical applications of GANs, making it an excellent resource for beginners. A Primer on Generative Adversarial Networks is suitable for researchers, developers, students, and anyone who wishes to learn about GANs. It is assumed that the reader has a basic understanding of machine learning and neural networks. The book comes with ready-to-run scripts that readers can use for further research. Python is used as the primary programming language, so readers should be familiar with its basics. The book starts by providing an overview of GAN architecture, explaining the concept of generative models. It then introduces the most straightforward GAN architecture, which explains how GANs work and covers the concepts of generator and discriminator. The book then goes into the more advanced real-world applications of GANs, such as human face generation, deep fake, CycleGANs, and more. By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners. 410 0$aSpringerBriefs in Computer Science,$x2191-5776 606 $aMachine learning 606 $aSignal processing 606 $aComputer simulation 606 $aMachine Learning 606 $aSignal, Speech and Image Processing 606 $aComputer Modelling 615 0$aMachine learning. 615 0$aSignal processing. 615 0$aComputer simulation. 615 14$aMachine Learning. 615 24$aSignal, Speech and Image Processing . 615 24$aComputer Modelling. 676 $a006.31 700 $aKaddoura$b Sanaa$01373785 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996546829703316 996 $aA Primer on Generative Adversarial Networks$93404904 997 $aUNISA