東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Generating a new reality = from auto...

Lanham, Micheal.

FindBook

Google Book

Amazon

博客來

Generating a new reality = from autoencoders and adversarial networks to deepfakes /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Generating a new reality/ by Micheal Lanham.
其他題名:	from autoencoders and adversarial networks to deepfakes /
作者:	Lanham, Micheal.
出版者:	Berkeley, CA :Apress : : 2021.,
面頁冊數:	xvii, 321 p. :ill., digital ;24 cm.
內容註:	Chapter 1: The Basics of Deep Learning -- Chapter 2: Unleashing Generative Modeling -- Chapter 3: Exploring the Latent Space -- Chapter 4: GANs, GANs, and More GANs -- Chapter 5: Image to Image Generation with GANs -- Chapter 6: Residual Network GANs -- Chapter 7: Attention Is All We Need -- Chapter 8: Advanced Generators -- Chapter 9: Deepfakes and Faceswapping -- Chapter 10: Cracking Deepfakes -- Appendix A: Running Google Colab Locally -- Appendix B: Opening a Notebook -- Appendix C: Connecting Google Drive and Saving.
Contained By:	Springer Nature eBook
標題:	Generative programming (Computer science) -
電子資源:	https://doi.org/10.1007/978-1-4842-7092-9
ISBN:	9781484270929

Generating a new reality = from autoencoders and adversarial networks to deepfakes /
Lanham, Micheal.

Generating a new realityfrom autoencoders and adversarial networks to deepfakes /[electronic resource] :by Micheal Lanham. - Berkeley, CA :Apress :2021. - xvii, 321 p. :ill., digital ;24 cm.

Chapter 1: The Basics of Deep Learning -- Chapter 2: Unleashing Generative Modeling -- Chapter 3: Exploring the Latent Space -- Chapter 4: GANs, GANs, and More GANs -- Chapter 5: Image to Image Generation with GANs -- Chapter 6: Residual Network GANs -- Chapter 7: Attention Is All We Need -- Chapter 8: Advanced Generators -- Chapter 9: Deepfakes and Faceswapping -- Chapter 10: Cracking Deepfakes -- Appendix A: Running Google Colab Locally -- Appendix B: Opening a Notebook -- Appendix C: Connecting Google Drive and Saving.

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. You will: 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, and StyleGAN2.

ISBN: 9781484270929

Standard No.: 10.1007/978-1-4842-7092-9doiSubjects--Topical Terms:

3501582
Generative programming (Computer science)

LC Class. No.: QA76.624 / .L36 2021

Dewey Class. No.: 005.11

Generating a new reality = from autoencoders and adversarial networks to deepfakes /
LDR:02760nmm a2200325 a 4500 001 2242426
003 DE-He213
005 20210715134428.0
006 m d
007 cr nn 008maaau
008 211207s2021 cau s 0 eng d
020 $a 9781484270929 $q (electronic bk.)
020 $a 9781484270912 $q (paper)
024 7 $a 10.1007/978-1-4842-7092-9 $2 doi
035 $a 978-1-4842-7092-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.624 $b .L36 2021
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 005.11 $2 23
090 $a QA76.624 $b .L287 2021
100 1 $a Lanham, Micheal. $3 3501581
245 1 0 $a Generating a new reality $h [electronic resource] : $b from autoencoders and adversarial networks to deepfakes / $c by Micheal Lanham.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a xvii, 321 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: The Basics of Deep Learning -- Chapter 2: Unleashing Generative Modeling -- Chapter 3: Exploring the Latent Space -- Chapter 4: GANs, GANs, and More GANs -- Chapter 5: Image to Image Generation with GANs -- Chapter 6: Residual Network GANs -- Chapter 7: Attention Is All We Need -- Chapter 8: Advanced Generators -- Chapter 9: Deepfakes and Faceswapping -- Chapter 10: Cracking Deepfakes -- Appendix A: Running Google Colab Locally -- Appendix B: Opening a Notebook -- Appendix C: Connecting Google Drive and Saving.
520 $a 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. You will: 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, and StyleGAN2.
650 0 $a Generative programming (Computer science) $3 3501582
650 0 $a Computer graphics. $3 517127
650 0 $a Artificial intelligence. $3 516317
650 1 4 $a Machine Learning. $3 3382522
650 2 4 $a Python. $3 3201289
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-7092-9
950 $a Professional and Applied Computing (SpringerNature-12059)