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Generative Adversarial Networks Projects : Build Next-Generation Generative Models Using TensorFlow and Keras.
In this book, we will use different complexities of datasets in order to build end-to-end projects. With every chapter, the level of complexity and operations will become advanced. It consists of 8 full-fledged projects covering approaches such as 3D-GAN, Age-cGAN, DCGAN, SRGAN, StackGAN, and CycleG...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Ahirwar, Kailash. | |
| 245 | 1 | 0 | |a Generative Adversarial Networks Projects : |b Build Next-Generation Generative Models Using TensorFlow and Keras. |
| 260 | |a Birmingham : |b Packt Publishing Ltd, |c 2019. | ||
| 300 | |a 1 online resource (310 pages) | ||
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| 505 | 0 | |a Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Generative Adversarial Networks; What is a GAN?; What is a generator network?; What is a discriminator network?; Training through adversarial play in GANs; Practical applications of GANs; The detailed architecture of a GAN; The architecture of the generator ; The architecture of the discriminator; Important concepts related to GANs; Kullback-Leibler divergence; Jensen-Shannon divergence; Nash equilibrium; Objective functions; Scoring algorithms; The inception score | |
| 505 | 8 | |a The Fréchet inception distanceVariants of GANs; Deep convolutional generative adversarial networks; StackGANs; CycleGANs; 3D-GANs; Age-cGANs; pix2pix; Advantages of GANs; Problems with training GANs; Mode collapse; Vanishing gradients; Internal covariate shift; Solving stability problems when training GANs; Feature matching; Mini-batch discrimination; Historical averaging; One-sided label smoothing; Batch normalization; Instance normalization; Summary; Chapter 2: 3D-GAN -- Generating Shapes Using GANs; Introduction to 3D-GANs; 3D convolutions; The architecture of a 3D-GAN | |
| 505 | 8 | |a The architecture of the generator networkThe architecture of the discriminator network; Objective function; Training 3D-GANs; Setting up a project; Preparing the data; Download and extract the dataset; Exploring the dataset; What is a voxel?; Loading and visualizing a 3D image; Visualizing a 3D image; A Keras implementation of a 3D-GAN; The generator network; The discriminator network; Training a 3D-GAN; Training the networks; Saving the models; Testing the models; Visualizing losses; Visualizing graphs; Hyperparameter optimization; Practical applications of 3D-GANs; Summary | |
| 505 | 8 | |a Chapter 3: Face Aging Using Conditional GANIntroducing cGANs for face aging; Understanding cGANs; The architecture of the Age-cGAN; The encoder network; The generator network; The discriminator network; Face recognition network; Stages of the Age-cGAN; Conditional GAN training; The training objective function; Initial latent vector approximation; Latent vector optimization; Setting up the project; Preparing the data; Downloading the dataset; Extracting the dataset; A Keras implementation of an Age-cGAN; The encoder network; The generator network; The discriminator network; Training the cGAN | |
| 505 | 8 | |a Training the cGANInitial latent vector approximation; Latent vector optimization; Visualizing the losses; Visualizing the graphs; Practical applications of Age-cGAN; Summary; Chapter 4: Generating Anime Characters Using DCGANs; Introducing to DCGANs; Architectural details of a DCGAN; Configuring the generator network; Configuring the discriminator network; Setting up the project; Downloading and preparing the anime characters dataset; Downloading the dataset; Exploring the dataset; Cropping and resizing images in the dataset; Implementing a DCGAN using Keras; Generator; Discriminator | |
| 500 | |a Training the DCGAN | ||
| 520 | |a In this book, we will use different complexities of datasets in order to build end-to-end projects. With every chapter, the level of complexity and operations will become advanced. It consists of 8 full-fledged projects covering approaches such as 3D-GAN, Age-cGAN, DCGAN, SRGAN, StackGAN, and CycleGAN with real-world use cases. | ||
| 504 | |a Includes bibliographical references. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 0 | |a Artificial intelligence. | |
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| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 6 | |a Intelligence artificielle. | |
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| 776 | 0 | 8 | |i Print version: |a Ahirwar, Kailash. |t Generative Adversarial Networks Projects : Build Next-Generation Generative Models Using TensorFlow and Keras. |d Birmingham : Packt Publishing Ltd, ©2019 |z 9781789136678 |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2018972 |z Texto completo |
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