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Titlebook: A Primer on Generative Adversarial Networks; Sanaa Kaddoura Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive lic

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发表于 2025-3-21 16:14:45 | 显示全部楼层 |阅读模式
期刊全称A Primer on Generative Adversarial Networks
影响因子2023Sanaa Kaddoura
视频video
发行地址A self-contained short and focused guide for practitioners and students beginning in GANs.Can be used by researchers for building new and attractive tools for several applications.Code-based presentat
学科分类SpringerBriefs in Computer Science
图书封面Titlebook: A Primer on Generative Adversarial Networks;  Sanaa Kaddoura Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive lic
影响因子This 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, rega
Pindex Book 2023
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https://doi.org/10.1007/978-1-4419-5675-0 networks: the generator and the discriminator are mainly made up of a deep neural network. The generator model aims to generate new data that looks like the real one. Typically, the generator model is composed of multiple up-sampling layers. The discriminator aimed to differentiate between actual d
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978-3-031-32660-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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2191-5768 ation, 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, rega978-3-031-32660-8978-3-031-32661-5Series ISSN 2191-5768 Series E-ISSN 2191-5776
发表于 2025-3-23 05:31:39 | 显示全部楼层
Overview of GAN Structure,n this case, in particular, feminist and gender-related research—was marginalized, as were the experts in the institution who carry that knowledge. Therefore, the World Bank’s evidence-based approach led to certifying a thin version of CCTs, minus any goals for altering gender relations or outcomes.
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