GANs in Action: Deep learning with Generative Adversarial Networks
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Language: English
Published by Manning Publications, 2019
Series: Book 124 of 182 - In Action
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- Title
- GANs in Action: Deep learning with Generative Adversarial Networks
- Author
- Langr, Jakub
- Publisher
- Manning Publications
- Publication year
- 2019
- Condition
- good
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1617295566
- ISBN 13
- 9781617295560
- Series
- Book 124 of 182: In Action
Summary
GANs in Action teaches you how to build and train your own Generative Adversarial Networks, one of the most important innovations in deep learning. In this book, you'll learn how to start building your own simple adversarial system as you explore the foundation of GAN architecture: the generator and discriminator networks.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Technology
Generative Adversarial Networks, GANs, are an incredible AI technology capable of creating images, sound, and videos that are indistinguishable from the "real thing." By pitting two neural networks against each other--one to generate fakes and one to spot them--GANs rapidly learn to produce photo-realistic faces and other media objects. With the potential to produce stunningly realistic animations or shocking deepfakes, GANs are a huge step forward in deep learning systems.
About the Book
GANs in Action teaches you to build and train your own Generative Adversarial Networks. You'll start by creating simple generator and discriminator networks that are the foundation of GAN architecture. Then, following numerous hands-on examples, you'll train GANs to generate high-resolution images, image-to-image translation, and targeted data generation. Along the way, you'll find pro tips for making your system smart, effective, and fast.
What's inside
For data professionals with intermediate Python skills, and the basics of deep learning-based image processing.
About the Author
Jakub Langr is working on ML tooling and was a Computer Vision Lead at Founders Factory. Vladimir Bok is a Senior Product Manager overseeing machine learning infrastructure and research teams at a New York-based startup.
Table of Contents
GANs in Action teaches you how to build and train your own Generative Adversarial Networks, one of the most important innovations in deep learning. In this book, you'll learn how to start building your own simple adversarial system as you explore the foundation of GAN architecture: the generator and discriminator networks.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Technology
Generative Adversarial Networks, GANs, are an incredible AI technology capable of creating images, sound, and videos that are indistinguishable from the "real thing." By pitting two neural networks against each other--one to generate fakes and one to spot them--GANs rapidly learn to produce photo-realistic faces and other media objects. With the potential to produce stunningly realistic animations or shocking deepfakes, GANs are a huge step forward in deep learning systems.
About the Book
GANs in Action teaches you to build and train your own Generative Adversarial Networks. You'll start by creating simple generator and discriminator networks that are the foundation of GAN architecture. Then, following numerous hands-on examples, you'll train GANs to generate high-resolution images, image-to-image translation, and targeted data generation. Along the way, you'll find pro tips for making your system smart, effective, and fast.
What's inside
- Building your first GAN
- Handling the progressive growing of GANs
- Practical applications of GANs
- Troubleshooting your system
For data professionals with intermediate Python skills, and the basics of deep learning-based image processing.
About the Author
Jakub Langr is working on ML tooling and was a Computer Vision Lead at Founders Factory. Vladimir Bok is a Senior Product Manager overseeing machine learning infrastructure and research teams at a New York-based startup.
Table of Contents
-
PART 1 - INTRODUCTION TO GANS AND GENERATIVE MODELING
- Introduction to GANs
- Intro to generative modeling with autoencoders
- Your first GAN: Generating handwritten digits
- Deep Convolutional GAN PART 2 - ADVANCED TOPICS IN GANS
- Training and common challenges: GANing for success
- Progressing with GANs
- Semi-Supervised GAN
- Conditional GAN
- CycleGANPART 3 - WHERE TO GO FROM HERE
- Adversarial examples
- Practical applications of GANs
- Looking ahead
"Synopsis" may belong to another edition of this title.
About the Author
Jakub Langr graduated from Oxford University where he also taught at OU Computing Services. He has worked in data science since 2013, most recently as a data science Tech Lead at Filtered.com and as a data science consultant at Mudano. Jakub also designed and teaches Data Science courses at the University of Birmingham and is a fellow of the Royal Statistical Society.
Vladimir Bok is a Senior Product Manager at Intent Media, a data science company for leading travel sites, where he helps oversee the company's Machine Learning research and infrastructure teams. Prior to that, he was a Program Manager at Microsoft. Vladimir graduated Cum Laude with a degree in Computer Science from Harvard University. He has worked as a software engineer at early stage FinTech companies, including one founded by PayPal co-founder Max Levchin, and as a Data Scientist at a Y Combinator startup.
Vladimir Bok is a Senior Product Manager at Intent Media, a data science company for leading travel sites, where he helps oversee the company's Machine Learning research and infrastructure teams. Prior to that, he was a Program Manager at Microsoft. Vladimir graduated Cum Laude with a degree in Computer Science from Harvard University. He has worked as a software engineer at early stage FinTech companies, including one founded by PayPal co-founder Max Levchin, and as a Data Scientist at a Y Combinator startup.
"About the title" may belong to another edition of this title.
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