000 03170nam a22005295i 4500
001 978-3-031-32661-5
003 DE-He213
005 20240423125547.0
007 cr nn 008mamaa
008 230704s2023 sz | s |||| 0|eng d
020 _a9783031326615
_9978-3-031-32661-5
024 7 _a10.1007/978-3-031-32661-5
_2doi
050 4 _aQ325.5-.7
072 7 _aUYQM
_2bicssc
072 7 _aMAT029000
_2bisacsh
072 7 _aUYQM
_2thema
082 0 4 _a006.31
_223
100 1 _aKaddoura, Sanaa.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 2 _aA Primer on Generative Adversarial Networks
_h[electronic resource] /
_cby Sanaa Kaddoura.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aX, 84 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5776
505 0 _aOverview of GAN Structure -- Your First GAN -- Real World Applications -- Conclusion.
520 _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.
650 0 _aMachine learning.
650 0 _aSignal processing.
650 0 _aComputer simulation.
650 1 4 _aMachine Learning.
650 2 4 _aSignal, Speech and Image Processing.
650 2 4 _aComputer Modelling.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031326608
776 0 8 _iPrinted edition:
_z9783031326622
830 0 _aSpringerBriefs in Computer Science,
_x2191-5776
856 4 0 _uhttps://doi.org/10.1007/978-3-031-32661-5
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c179288
_d179288