Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras by Kailash Ahirwar (PDF)

Description

Explore various Generative Adversarial Network architectures using the Python ecosystemKey FeaturesUse 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 learnTrain 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 KerasWho 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. Table of ContentsIntroduction to Generative Adversarial Networks3D-GAN – Generating Shapes Using GANsFace Aging Using Conditional GANGenerating Anime Characters Using DCGANsUsing SRGANs to Generate Photo-Realistic ImagesStackGAN- Text to Photo-Realistic Image SynthesisCycleGAN- Turn Paintings into PhotosConditional GAN – Image-to-Image Translation Using Conditional Adversarial NetworksPredicting the Future of GANs

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐Great comprehensive book on some basic GAN techniques.

⭐Not really useful or informative as a stand-alone body of work. The external dependencies actually make this completely superfluous and superficial.

⭐Book doesn’t actually explain how the code works in detail or what each line does, it tells you an overall of what the code does. Once coded up, the code didn’t work and when issues are opened on the books GitHub repo, the author is unresponsive. Would not recommend this book.

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Free Download Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras in PDF format
Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras PDF Free Download
Download Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras PDF Free
Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras PDF Free Download
Download Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras PDF
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