Books > Computing & IT > General theory of computing > Data structures
|
Buy Now
Generative Adversarial Networks Projects - Build next-generation generative models using TensorFlow and Keras (Paperback)
Loot Price: R1,268
Discovery Miles 12 680
|
|
Generative Adversarial Networks Projects - Build next-generation generative models using TensorFlow and Keras (Paperback)
Expected to ship within 10 - 15 working days
|
Explore various Generative Adversarial Network architectures using
the Python ecosystem Key Features Use different datasets to build
advanced projects in the Generative Adversarial Network domain
Implement projects ranging from generating 3D shapes to a face
aging application Explore the power of GANs to contribute in open
source research and projects Book DescriptionGenerative Adversarial
Networks (GANs) have the potential to build next-generation models,
as they can mimic any distribution of data. Major research and
development work is being undertaken in this field since it is one
of the rapidly growing areas of machine learning. This book will
test unsupervised techniques for training neural networks as you
build seven end-to-end projects in the GAN domain. Generative
Adversarial Network Projects begins by covering the concepts,
tools, and libraries that you will use to build efficient projects.
You will also use a variety of datasets for the different projects
covered in the book. The level of complexity of the operations
required increases with every chapter, helping you get to grips
with using GANs. You will cover popular approaches such as 3D-GAN,
DCGAN, StackGAN, and CycleGAN, and you'll gain an understanding of
the architecture and functioning of generative models through their
practical implementation. By the end of this book, you will be
ready to build, train, and optimize your own end-to-end GAN models
at work or in your own projects. What you will learn Train a
network on the 3D ShapeNet dataset to generate realistic shapes
Generate anime characters using the Keras implementation of DCGAN
Implement an SRGAN network to generate high-resolution images Train
Age-cGAN on Wiki-Cropped images to improve face verification Use
Conditional GANs for image-to-image translation Understand the
generator and discriminator implementations of StackGAN in Keras
Who this book is forIf you're a data scientist, machine learning
developer, deep learning practitioner, or AI enthusiast looking for
a project guide to test your knowledge and expertise in building
real-world GANs models, this book is for you.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
|
You might also like..
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.