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Titlebook: Generating a New Reality; From Autoencoders an Micheal Lanham Book 2021 Micheal Lanham 2021 Generative Adversarial Networks.Deepfake.Self A

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發(fā)表于 2025-3-21 18:39:29 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Generating a New Reality
副標(biāo)題From Autoencoders an
編輯Micheal Lanham
視頻videohttp://file.papertrans.cn/383/382278/382278.mp4
概述Explores variations of content generation AI, not just GANs.Uses free online resources (such as Google Collaboratory) that allow users to train AI with GPUs on the cloud.Is developer-focused, with lot
圖書封面Titlebook: Generating a New Reality; From Autoencoders an Micheal Lanham Book 2021 Micheal Lanham 2021 Generative Adversarial Networks.Deepfake.Self A
描述The emergence of artificial intelligence (AI) has brought us to the precipice of a new age where we struggle to understand what is real, from advanced CGI in movies to even faking the news. AI that was developed to understand our reality is now being used to create its own reality.?.In this book we look at the many AI techniques capable of generating new realities. We start with the basics of deep learning. Then we move on to autoencoders and generative adversarial networks (GANs). We explore variations of GAN to generate content. The book ends with an in-depth look at the most popular generator projects..By the end of this book you will understand the AI techniques used to generate different forms of content. You will be able to use these techniques for your own amusement or professional career to both impress and educate others around you and give you the ability to transform your own reality into something new..What You Will Learn.Know the fundamentals of content generation from autoencoders to generative adversarial networks (GANs).Explore variations of GAN.Understand the basics of other forms of content generation.Use advanced projects such as Faceswap, deepfakes, DeOldify, an
出版日期Book 2021
關(guān)鍵詞Generative Adversarial Networks; Deepfake; Self Attention GAN; Autoencoders; DeOldify; Avatarify; First Or
版次1
doihttps://doi.org/10.1007/978-1-4842-7092-9
isbn_softcover978-1-4842-7091-2
isbn_ebook978-1-4842-7092-9
copyrightMicheal Lanham 2021
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 22:13:11 | 只看該作者
n from autoencoders to generative adversarial networks (GANs).Explore variations of GAN.Understand the basics of other forms of content generation.Use advanced projects such as Faceswap, deepfakes, DeOldify, an978-1-4842-7091-2978-1-4842-7092-9
板凳
發(fā)表于 2025-3-22 03:49:37 | 只看該作者
Andre Bafica,Julio Aliberti Ph.D.nd then to the Renaissance, our interpretation of reality has matured over time. What we once perceived as mysticism is now understood and regulated by much of science. Not more than 10 years ago we were on track to understanding the reality of the universe, or so we thought. Now, with the inception
地板
發(fā)表于 2025-3-22 08:14:00 | 只看該作者
https://doi.org/10.1007/978-1-4614-1833-7plosion of growth has fostered in a new wave of AI. AI has matured from using supervised learning to more advanced forms of learning including unsupervised, self-supervised learning, adversarial learning, and reinforcement learning. It was from these other forms of learning that the area of generati
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發(fā)表于 2025-3-22 12:04:00 | 只看該作者
Control Analysis: a Theory that Worksadversarial network. The results of these deep learning systems can appear magical and even show signs of actual intelligence. Unfortunately, the truth is far different and even challenges our perception of intelligence.
6#
發(fā)表于 2025-3-22 15:05:27 | 只看該作者
Positive Position Feedback (PPF) Control,adversarial network (GAN). There is some debate on when GANs were discovered and by whom. One thing is for certain: Ian Goodfellow and his colleagues from the University of Montreal in 2014 deserve a good deal of credit for reinventing the technique of adversarial learning.
7#
發(fā)表于 2025-3-22 19:19:29 | 只看該作者
Peripheral Arterial Chemoreceptors,ugh the course of this book, we have covered the history of those generators as they have progressed. We looked closely at the details and technical advances of their contribution to generation, as well as how they may have failed.
8#
發(fā)表于 2025-3-22 21:19:44 | 只看該作者
B. B. Biswas,R. K. Mandal,W. E. Cohnique is colloquially known as . and has been the basis for fake news and all forms of various related conspiracy theories. Many, from fear of understanding, see this technology as providing no value and as unethical, which also gives generative modeling a bad image.
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發(fā)表于 2025-3-23 03:47:37 | 只看該作者
10#
發(fā)表于 2025-3-23 06:54:09 | 只看該作者
https://doi.org/10.1007/978-1-4842-7092-9Generative Adversarial Networks; Deepfake; Self Attention GAN; Autoencoders; DeOldify; Avatarify; First Or
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