Picture 1 of 2

Stock photo

Gans in Action: Deep Learning with Generative Adversarial Networks [Paperback] - Picture 1 of 2

Gans in Action: Deep Learning with Generative Adversarial Networks [Paperback] - Picture 2 of 2

GANs in Action : Deep Learning with Generative Adversarial Networks by Jakub Langr and Vladimir Bok (2019, Trade Paperback)

loveourprices2 (121925)

94.7% positive feedback

Price:

$54.38

Free shipping

Estimated delivery Wed, Oct 15 - Fri, Oct 24

Returns:

60 days returns. Buyer pays for return shipping. If you use an eBay shipping label, it will be deducted from your refund amount.

Condition:

Brand New

GANs in Action: Deep learning with Generative Adversarial Networks teaches you how to build and train your own generative adversarial networks. Then, you'll start building your own simple adversarial system, as you explore the foundation of GAN architecture: the generator and discriminator networks.

About this product

Product Identifiers

PublisherManning Publications Co. LLC

ISBN-101617295566

ISBN-139781617295560

eBay Product ID (ePID)19038375774

Product Key Features

Number of Pages276 Pages

LanguageEnglish

Publication NameGans in Action : Deep Learning with Generative Adversarial Networks

Publication Year2019

SubjectOptical Data Processing, Intelligence (Ai) & Semantics, Neural Networks

TypeTextbook

Subject AreaComputers

AuthorJakub Langr, Vladimir Bok

FormatTrade Paperback

Dimensions

Item Height0.4 in

Item Weight14.9 Oz

Item Length9.2 in

Item Width7.4 in

Additional Product Features

Intended AudienceScholarly & Professional

LCCN2019-286864

Dewey Edition23

IllustratedYes

Dewey Decimal006.31

SynopsisDeep learning systems have gotten really great at identifying patterns in text, images, and video. But applications that create realistic images, natural sentences and paragraphs, or native-quality translations have proven elusive. Generative Adversarial Networks, or GANs, offer a promising solution to these challenges by pairing two competing neural networks--one that generates content and the other that rejects samples that are of poor quality. GANs in Action: Deep learning with Generative Adversarial Networks teaches you how to build and train your own generative adversarial networks. First, you'll get an introduction to generative modelling and how GANs work, along with an overview of their potential uses. Then, you'll start building your own simple adversarial system, as you explore the foundation of GAN architecture: the generator and discriminator networks. Key Features - Understanding GANs and their potential - Hands-on code tutorials to build GAN models - Advanced GAN architectures and techniques like Cycle-Consistent Adversarial Networks - Handling the progressive growing of GANs - Practical applications of GANs Written for data scientists and data analysts with intermediate Python knowledge. Knowing the basics of deep learning will also be helpful. About the technology GANs have already achieved remarkable results that have been thought impossible for artificial systems, such as the ability to generate realistic faces, turn a scribble into a photograph-like image, are turn video footage of a horse into a running zebra. Most importantly, GANs learn quickly without the need for vast troves of painstakingly labeled training data. 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., 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. About the Author 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. 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, Deep learning systems have gotten really great at identifying patterns in text, images, and video. But applications that create realistic images, natural sentences and paragraphs, or native-quality translations have proven elusive. Generative Adversarial Networks, or GANs, offer a promising solution to these challenges by pairing two competing neural networks--one that generates content and the other that rejects samples that are of poor quality. GANs in Action: Deep learning with Generative Adversarial Networks teaches you how to build and train your own generative adversarial networks. First, you'll get an introduction to generative modelling and how GANs work, along with an overview of their potential uses. Then, you'll start building your own simple adversarial system, as you explore the foundation of GAN architecture: the generator and discriminator networks. Key Features · Understanding GANs and their potential · Hands-on code tutorials to build GAN models · Advanced GAN architectures and techniques like Cycle-Consistent Adversarial Networks · Handling the progressive growing of GANs · Practical applications of GANs Written for data scientists and data analysts with intermediate Python knowledge. Knowing the basics of deep learning will also be helpful. About the technology GANs have already achieved remarkable results that have been thought impossible for artificial systems, such as the ability to generate realistic faces, turn a scribble into a photograph-like image, are turn video footage of a horse into a running zebra. Most importantly, GANs learn quickly without the need for vast troves of painstakingly labeled training data. 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., Deep learning systems have gotten really great at identifying patterns in text, images, and video. But applications that create realistic images, natural sentences and paragraphs, or native-quality translations have proven elusive. Generative Adversarial Networks, or GANs, offer a promising solution to these challenges by pairing two competing neural networks' one that generates content and the other that rejects samples that are of poor quality. GANs in Action: Deep learning with Generative Adversarial Networks teaches you how to build and train your own generative adversarial networks. First, you'll get an introduction to generative modelling and how GANs work, along with an overview of their potential uses. Then, you'll 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.

LC Classification NumberQ325.5.L36 2019