000 | 03376nam a22005535i 4500 | ||
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001 | 978-3-031-32661-5 | ||
003 | DE-He213 | ||
005 | 20240207153631.0 | ||
007 | cr nn 008mamaa | ||
008 | 230704s2023 sz | s |||| 0|eng d | ||
020 |
_a9783031326615 _9978-3-031-32661-5 |
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050 | 4 | _aQ325.5-.7 | |
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_a006.31 _223 |
100 | 1 |
_aKaddoura, Sanaa. _eauthor. _0(orcid)0000-0002-4384-4364 _1https://orcid.org/0000-0002-4384-4364 _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 |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aSpringerBriefs in Computer Science, _x2191-5776 |
|
500 | _aAcceso multiusuario | ||
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. | ||
541 |
_fUABC ; _cPerpetuidad |
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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 |
_zLibro electrónico _uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-32661-5 |
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912 | _aZDB-2-SXCS | ||
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999 |
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