000			02872nam a22003855i 4500
999			_c398242 _d398242 _x1
001			398242
003			ES-MaUEC
005			20240429180402.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			230704s2023 sz | o |||| 0|eng d
020			_a9783031326615
024	7		_a10.1007/978-3-031-32661-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.87 _b2023 EB
100	1		_aKaddoura, Sanaa, _eautor _0(orcid)0000-0002-4384-4364 _1https://orcid.org/0000-0002-4384-4364 _4http://id.loc.gov/vocabulary/relators/aut _9689648 _d1986-
245	1	2	_aA Primer on Generative Adversarial Networks _cby Sanaa Kaddoura
250			_a1st ed 2023
264		1	_aCham _bSpringer International Publishing _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_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.
988			_aSpringer_Computer_2023
650		7	_2embne _9678664 _aRedes neuronales artificiales
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-32661-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _ek _zSI