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Generative Adversarial Networks Cookbook - Over 100 recipes to build generative models using Python, TensorFlow, and Keras (Paperback)
Loot Price: R1,206
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Generative Adversarial Networks Cookbook - Over 100 recipes to build generative models using Python, TensorFlow, and Keras (Paperback)
Expected to ship within 10 - 15 working days
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Simplify next-generation deep learning by implementing powerful
generative models using Python, TensorFlow and Keras Key Features
Understand the common architecture of different types of GANs
Train, optimize, and deploy GAN applications using TensorFlow and
Keras Build generative models with real-world data sets, including
2D and 3D data Book DescriptionDeveloping Generative Adversarial
Networks (GANs) is a complex task, and it is often hard to find
code that is easy to understand. This book leads you through eight
different examples of modern GAN implementations, including
CycleGAN, simGAN, DCGAN, and 2D image to 3D model generation. Each
chapter contains useful recipes to build on a common architecture
in Python, TensorFlow and Keras to explore increasingly difficult
GAN architectures in an easy-to-read format. The book starts by
covering the different types of GAN architecture to help you
understand how the model works. This book also contains intuitive
recipes to help you work with use cases involving DCGAN, Pix2Pix,
and so on. To understand these complex applications, you will take
different real-world data sets and put them to use. By the end of
this book, you will be equipped to deal with the challenges and
issues that you may face while working with GAN models, thanks to
easy-to-follow code solutions that you can implement right away.
What you will learn Structure a GAN architecture in pseudocode
Understand the common architecture for each of the GAN models you
will build Implement different GAN architectures in TensorFlow and
Keras Use different datasets to enable neural network functionality
in GAN models Combine different GAN models and learn how to
fine-tune them Produce a model that can take 2D images and produce
3D models Develop a GAN to do style transfer with Pix2Pix Who this
book is forThis book is for data scientists, machine learning
developers, and deep learning practitioners looking for a quick
reference to tackle challenges and tasks in the GAN domain.
Familiarity with machine learning concepts and working knowledge of
Python programming language will help you get the most out of the
book.
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