Gans in Action pdf epub mobi txt 電子書 下載2025
Jakub Langr is a Computer Vision Cofounder at Founders Factory (YEPIC.AI). Vladimir Bok is a Senior Product Manager overseeing machine learning infrastructure and research teams at a New York-based startup.
- GAN
- AI
- 計算機
- 深度學習
- 計算機科學
- DeepLearning
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
Building your first GAN
Handling the progressive growing of GANs
Practical applications of GANs
Troubleshooting your system
About the Reader
For data professionals with intermediate Python skills, and the basics of deep learning-based image processing.
具體描述
讀後感
評分
評分
評分
評分
用戶評價
對GAN世界的瞭解大增瞭。
评分對GAN世界的瞭解大增瞭。
评分對GAN世界的瞭解大增瞭。
评分對GAN世界的瞭解大增瞭。
评分對GAN世界的瞭解大增瞭。
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