Generative Adversarial Networks With Industrial Use Cases

Best Book on GAN Ê DESCRIPTIONÊ This book aims at simplifying GAN for everyone. This book is very important for machine learning engineers, researchers, students, professors, and professionals. Universities and online course instructors will find this book very interesting for teaching advanced deep learning, specially Generative Adversarial Networks(GAN). Industry professionals, coders, and data scientists can learn GAN from scratch. They can learn how to build GAN codes for industrial applications for Healthcare, Retail, HRTech, EduTech, Telecom, Media, and Entertainment. Mathematics of GAN is discussed and illustrated. KL divergence and other parts of GAN are illustrated and discussed mathematically. This book teaches how to build codes for pix2pix GAN, DCGAN, CGAN, styleGAN, cycleGAN, and many other GAN. Machine Learning and Deep Learning Researchers will learn GAN in the shortest possible time with the help of this book. Ê KEY FEATURESÊÊ - Understanding the deep learning landscape and GANÕs relevance - Learning basics of GAN - Learning how to build GAN from scratch - Understanding mathematics and limitations of GAN - Understanding GAN applications for Retail, Healthcare, Telecom, Media and EduTech - Understanding the important GAN papers such as pix2pixGAN, styleGAN, cycleGAN, DCGAN - Learning how to build GAN code for industrial applications - Understanding the difference between varieties of GAN WHAT WILL YOU LEARNÊ _ÊMachine Learning Researchers would be comfortable in building advanced deep learning codes for Industrial applications _ÊData Scientists would start solving very complex problems in deep learning _ÊStudents would be ready to join an industry with these skills _ÊAverage data engineers and scientists would be able to develop complex GAN codes to solve the toughest problems in computer vision Ê WHO THIS BOOK IS FORÊÊ This book is perfect for machine learning engineers, data scientists, data engineers, deep learning professionals, and computer vision researchers. This book is also very useful for medical imaging professionals, autonomous vehicles professionals, retail fashion professionals, media & entertainment professionals, edutech and HRtech professionals. Professors and Students working in machine learning, deep learning, computer vision, and industrial applications would find this book extremely useful. TABLE OF CONTENTS 1. Basics of GAN 2. Introduction 3. Problem with GANÊ 4. Famous Types Of GANs

This book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. This book's major goal is to concentrate on cutting-edge research in deep learning and generative adversarial networks, which includes creating new tools and methods for processing text, images, and audio. A Generative Adversarial Network (GAN) is a class of machine learning framework and is the next emerging network in deep learning applications. Generative Adversarial Networks(GANs) have the feasibility to build improved models, as they can generate the sample data as per application requirements. There are various applications of GAN in science and technology, including computer vision, security, multimedia and advertisements, image generation, image translation,text-to-images synthesis, video synthesis, generating high-resolution images, drug discovery, etc. Features: Presents a comprehensive guide on how to use GAN for images and videos. Includes case studies of Underwater Image Enhancement Using Generative Adversarial Network, Intrusion detection using GAN Highlights the inclusion of gaming effects using deep learning methods Examines the significant technological advancements in GAN and its real-world application. Discusses as GAN challenges and optimal solutions The book addresses scientific aspects for a wider audience such as junior and senior engineering, undergraduate and postgraduate students, researchers, and anyone interested in the trends development and opportunities in GAN and Deep Learning. The material in the book can serve as a reference in libraries, accreditation agencies, government agencies, and especially the academic institution of higher education intending to launch or reform their engineering curriculum

Simplify next-generation deep learning by implementing powerful generative models using Python, TensorFlow and Keras Key FeaturesUnderstand the common architecture of different types of GANsTrain, optimize, and deploy GAN applications using TensorFlow and KerasBuild generative models with real-world data sets, including 2D and 3D dataBook Description Developing 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 learnStructure a GAN architecture in pseudocodeUnderstand the common architecture for each of the GAN models you will buildImplement different GAN architectures in TensorFlow and KerasUse different datasets to enable neural network functionality in GAN modelsCombine different GAN models and learn how to fine-tune themProduce a model that can take 2D images and produce 3D modelsDevelop a GAN to do style transfer with Pix2PixWho this book is for This 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.

Explore various Generative Adversarial Network architectures using the Python ecosystem Key FeaturesUse different datasets to build advanced projects in the Generative Adversarial Network domainImplement projects ranging from generating 3D shapes to a face aging applicationExplore the power of GANs to contribute in open source research and projectsBook Description Generative 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 shapesGenerate anime characters using the Keras implementation of DCGANImplement an SRGAN network to generate high-resolution imagesTrain Age-cGAN on Wiki-Cropped images to improve face verificationUse Conditional GANs for image-to-image translationUnderstand the generator and discriminator implementations of StackGAN in KerasWho this book is for If 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.

Develop generative models for a variety of real-world use cases and deploy them to production Key Features Discover various GAN architectures using a Python and Keras library Understand how GAN models function with the help of theoretical and practical examples Apply your learnings to become an active contributor to open source GAN applications Book Description Generative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning. This book will be your first step toward understanding GAN architectures and tackling the challenges involved in training them. This book opens with an introduction to deep learning and generative models and their applications in artificial intelligence (AI). You will then learn how to build, evaluate, and improve your first GAN with the help of easy-to-follow examples. The next few chapters will guide you through training a GAN model to produce and improve high-resolution images. You will also learn how to implement conditional GANs that enable you to control characteristics of GAN output. You will build on your knowledge further by exploring a new training methodology for progressive growing of GANs. Moving on, you'll gain insights into state-of-the-art models in image synthesis, speech enhancement, and natural language generation using GANs. In addition to this, you'll be able to identify GAN samples with TequilaGAN. By the end of this book, you will be well-versed with the latest advancements in the GAN framework using various examples and datasets, and you will have developed the skills you need to implement GAN architectures for several tasks and domains, including computer vision, natural language processing (NLP), and audio processing. Foreword by Ting-Chun Wang, Senior Research Scientist, NVIDIA What you will learn Discover how GANs work and the advantages and challenges of working with them Control the output of GANs with the help of conditional GANs, using embedding and space manipulation Apply GANs to computer vision, natural language processing (NLP), and audio processing Understand how to implement progressive growing of GANs Use GANs for image synthesis and speech enhancement Explore the future of GANs in visual and sonic arts Implement pix2pixHD to turn semantic label maps into photorealistic images Who this book is for This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking for a mix of theory and hands-on co ...

Develop generative models for a variety of real-world use-cases and deploy them to production Key FeaturesDiscover various GAN architectures using Python and Keras libraryUnderstand how GAN models function with the help of theoretical and practical examplesApply your learnings to become an active contributor to open source GAN applicationsBook Description Generative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning. This book will be your first step towards understanding GAN architectures and tackling the challenges involved in training them. This book opens with an introduction to deep learning and generative models, and their applications in artificial intelligence (AI). You will then learn how to build, evaluate, and improve your first GAN with the help of easy-to-follow examples. The next few chapters will guide you through training a GAN model to produce and improve high-resolution images. You will also learn how to implement conditional GANs that give you the ability to control characteristics of GAN outputs. You will build on your knowledge further by exploring a new training methodology for progressive growing of GANs. Moving on, you'll gain insights into state-of-the-art models in image synthesis, speech enhancement, and natural language generation using GANs. In addition to this, you'll be able to identify GAN samples with TequilaGAN. By the end of this book, you will be well-versed with the latest advancements in the GAN framework using various examples and datasets, and you will have the skills you need to implement GAN architectures for several tasks and domains, including computer vision, natural language processing (NLP), and audio processing. Foreword by Ting-Chun Wang, Senior Research Scientist, NVIDIA What you will learnLearn how GANs work and the advantages and challenges of working with themControl the output of GANs with the help of conditional GANs, using embedding and space manipulationApply GANs to computer vision, NLP, and audio processingUnderstand how to implement progressive growing of GANsUse GANs for image synthesis and speech enhancementExplore the future of GANs in visual and sonic artsImplement pix2pixHD to turn semantic label maps into photorealistic imagesWho this book is for This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking for a perfect mix of theory and hands-on content in order to implement GANs using Keras. Working knowledge of Python is expected.

Build image generation and semi-supervised models using Generative Adversarial NetworksAbout This Book* Understand the buzz surrounding Generative Adversarial Networks and how they work, in the simplest manner possible* Develop generative models for a variety of real-world use-cases and deploy them to production* Contains intuitive examples and real-world cases to put the theoretical concepts explained in this book to practical useWho This Book Is ForData scientists and machine learning practitioners who wish to understand the fundamentals of generative models will find this book useful. Those who wish to implement Generative Adversarial Networks and their variant architectures through real-world examples will also benefit from this book. No prior knowledge of generative models or GANs is expected.What You Will Learn* Understand the basics of deep learning and the difference between discriminative and generative models* Generate images and build semi-supervised models using Generative Adversarial Networks (GANs) with real-world datasets* Tune GAN models by addressing the challenges such as mode collapse, training instability using mini batch, feature matching, and the boundary equilibrium technique.* Use stacking with Deep Learning architectures to run and generate images from text.* Couple multiple Generative models to discover relationships across various domains* Explore the real-world steps to deploy deep models in productionIn DetailGenerative models are gaining a lot of popularity among the data scientists, mainly because they facilitate the building of AI systems that consume raw data from a source and automatically builds an understanding of it. Unlike supervised learning methods, generative models do not require labeling of the data which makes it an interesting system to use. This book will help you to build and analyze the deep learning models and apply them to real-world problems. This book will help readers develop intelligent and creative application from a wide variety of datasets, mainly focusing on visuals or images.The book begins with the basics of generative models, as you get to know the theory behind Generative Adversarial Networks and its building blocks. This book will show you how you can overcome the problem of text to image synthesis with GANs, using libraries like Tensorflow, Keras and PyTorch. Transfering style from one domain to another becomes a headache when working with huge data sets. The author, using real-world examples, will show how you can overcome this. You will understand and train Generative Adversarial Networks and use them in a production environment and learn tips to use them effectively and accurately.Style and approachA step-by-step guide that will teach you the use of appropriate GAN models for image generation, editing and painting, text-to-image synthesis, image style transfer, and cross-domain discovery with Python libraries such as Tensorflow, Keras, and PyTorch.

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.