Download or read book A Primer on Generative Adversarial Networks written by Sanaa Kaddoura and published by Springer Nature. This book was released on 2023-07-04 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is meant for readers who want to understand GANs without the need for a strong mathematical background. Moreover, it covers the practical applications of GANs, making it an excellent resource for beginners. A Primer on Generative Adversarial Networks is suitable for researchers, developers, students, and anyone who wishes to learn about GANs. It is assumed that the reader has a basic understanding of machine learning and neural networks. The book comes with ready-to-run scripts that readers can use for further research. Python is used as the primary programming language, so readers should be familiar with its basics. The book starts by providing an overview of GAN architecture, explaining the concept of generative models. It then introduces the most straightforward GAN architecture, which explains how GANs work and covers the concepts of generator and discriminator. The book then goes into the more advanced real-world applications of GANs, such as human face generation, deep fake, CycleGANs, and more. By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners.

Download or read book GANs in Action written by Vladimir Bok and published by Simon and Schuster. This book was released on 2019-09-09 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Generative Adversarial Networks Projects written by Kailash Ahirwar and published by Packt Publishing Ltd. This book was released on 2019-01-31 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Hands On Generative Adversarial Networks with Keras written by Rafael Valle and published by Packt Publishing Ltd. This book was released on 2019-05-03 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Generative Adversarial Networks Cookbook written by Josh Kalin and published by Packt Publishing Ltd. This book was released on 2018-12-31 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Casey Reas Making Pictures with Generative Adversarial Networks written by Casey Reas and published by . This book was released on 2019-09 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this first non-technical introduction to emerging AI techniques, artist Casey Reas explores what it's like to make pictures with generative adversarial networks (GANs), specifically deep convolutional generative adversarial networks (DCGANs). This text is imagined as a primer for readers interested in creative applications of AI technologies. Ideally, readers will explore the strategies of this emerging field as outlined, and remix them to suit their desires. We hope to inspire future research and collaboration, and to encourage a rigorous discussion about art in the age of machine intelligence.

Download or read book Generative Adversarial Networks GANs written by Russ Elektran and published by Independently Published. This book was released on 2024-04-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the power of Generative Adversarial Networks (GANs) with this comprehensive guidebook, designed to take you from a basic understanding to mastering the art and science behind these transformative neural networks. Whether you're a student, researcher, or professional in computer science and artificial intelligence, this book offers an accessible yet thorough exploration of GANs, covering foundational concepts, mathematical principles, diverse architectures, and ground-breaking applications. "Generative Adversarial Networks (GANs)" demystifies complex ideas through a structured presentation, starting with an introduction to GANs, diving into their mathematical underpinnings, and unfolding their architectural intricacies. Learn the best practices for training GANs, navigating common challenges, and evaluating performance to ensure high-quality outcomes. The book not only explains the various types of GANs and their specific uses but also showcases their incredible potential across different sectors-from creating realistic images to advancing drug discovery and beyond. With a step-by-step guide to building your own GAN model, this book empowers you to put theory into practice. It addresses common pitfalls, offers solutions to typical challenges, and provides insights into advanced topics for those looking to push the limits of what GANs can achieve. Whether you're aiming to understand the basic mechanisms of GANs or explore the frontiers of artificial intelligence research, this book is your go-to resource for all things GANs. Embark on this learning journey to leverage the full capabilities of Generative Adversarial Networks and unlock new possibilities in AI and machine learning.

Download or read book Generative Adversarial Learning Architectures and Applications written by Roozbeh Razavi-Far and published by Springer Nature. This book was released on 2022-03-11 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs’ theoretical developments and their applications.

Download or read book Hands On Generative Adversarial Networks with PyTorch 1 x written by John Hany and published by Packt Publishing Ltd. This book was released on 2019-12-12 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Apply deep learning techniques and neural network methodologies to build, train, and optimize generative network models Key FeaturesImplement GAN architectures to generate images, text, audio, 3D models, and moreUnderstand how GANs work and become an active contributor in the open source communityLearn how to generate photo-realistic images based on text descriptionsBook Description With continuously evolving research and development, Generative Adversarial Networks (GANs) are the next big thing in the field of deep learning. This book highlights the key improvements in GANs over generative models and guides in making the best out of GANs with the help of hands-on examples. This book starts by taking you through the core concepts necessary to understand how each component of a GAN model works. You'll build your first GAN model to understand how generator and discriminator networks function. As you advance, you'll delve into a range of examples and datasets to build a variety of GAN networks using PyTorch functionalities and services, and become well-versed with architectures, training strategies, and evaluation methods for image generation, translation, and restoration. You'll even learn how to apply GAN models to solve problems in areas such as computer vision, multimedia, 3D models, and natural language processing (NLP). The book covers how to overcome the challenges faced while building generative models from scratch. Finally, you'll also discover how to train your GAN models to generate adversarial examples to attack other CNN and GAN models. By the end of this book, you will have learned how to build, train, and optimize next-generation GAN models and use them to solve a variety of real-world problems. What you will learnImplement PyTorch's latest features to ensure efficient model designingGet to grips with the working mechanisms of GAN modelsPerform style transfer between unpaired image collections with CycleGANBuild and train 3D-GANs to generate a point cloud of 3D objectsCreate a range of GAN models to perform various image synthesis operationsUse SEGAN to suppress noise and improve the quality of speech audioWho this book is for This GAN book is for machine learning practitioners and deep learning researchers looking to get hands-on guidance in implementing GAN models using PyTorch. You’ll become familiar with state-of-the-art GAN architectures with the help of real-world examples. Working knowledge of Python programming language is necessary to grasp the concepts covered in this book.

Download or read book Generative Adversarial Networks with Python written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-07-11 with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step tutorials on generative adversarial networks in python for image synthesis and image translation.

Download or read book Generative Adversarial Networks with Industrial Use Cases written by Navin K Manaswi and published by BPB Publications. This book was released on 2020-03-04 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Generative Deep Learning written by David Foster and published by "O'Reilly Media, Inc.". This book was released on 2022-06-28 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos Train GANs to generate images based on your own dataset Build diffusion models to produce new varieties of flowers Train your own GPT for text generation Learn how large language models like ChatGPT are trained Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN Compose polyphonic music using Transformers and MuseGAN Understand how generative world models can solve reinforcement learning tasks Dive into multimodal models such as DALL.E 2, Imagen, and Stable Diffusion This book also explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create competitive advantage.

Download or read book Learning Generative Adversarial Networks written by Kuntal Ganguly and published by . This book was released on 2017-10-30 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book The GAN Book written by Kartik Chaudhary and published by Kartik Chaudhary (Drops of AI). This book was released on with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Key Features - Learn generative learning approach of ML and its key differences from the discriminative learning approach. - Understand why GANs are difficult to train, and key techniques to make their training stable to get impressive results. - Implement multiple variants of GANs for solving problems such as image generation, image-to-image translation, image super- resolution and so on. Book Description Generative Adversarial Networks have become quite popular due to their wide variety of applications in the fields of Computer Vision, Digital Marketing, Creative artwork and so on. One key challenge with GANs is that they are very difficult to train. This book is a comprehensive guide that highlights the common challenges of training GANs and also provides guidelines for developing GANs in such a way that they result in stable training and high-quality results. This book also explains the generative learning approach of training ML models and its key differences from the discriminative learning approach. After covering the different generative learning approaches, this book deeps dive more into the Generative Adversarial Network and their key variants. This book takes a hands-on approach and implements multiple generative models such as Pixel CNN, VAE, GAN, DCGAN, CGAN, SGAN, InfoGAN, ACGAN, WGAN, LSGAN, WGAN-GP, Pix2Pix, CycleGAN, SRGAN, DiscoGAN, CartoonGAN, Context Encoder and so on. It also provides a detailed explanation of some advanced GAN variants such as BigGAN, PGGAN, StyleGAN and so on. This book will make you a GAN champion in no time. What will you learn - Learn about the generative learning approach of training ML models - Understand key differences of the generative learning approach from the discriminative learning approach - Learn about various generative learning approaches and key technical aspects behind them - Understand and implement the Generative Adversarial Networks in details - Learn about some key challenges faced during GAN training and two common training failure modes - Build expertise in the best practices and guidelines for developing and training stable GANs - Implement multiple variants of GANs and verify their results on your own datasets - Learn about the adversarial examples, some key applications of GANs and common evaluation strategies Who this book is for If you are a ML practitioner who wants to learn about generative learning approaches and get expertise in Generative Adversarial Networks for generating high-quality and realistic content, this book is for you. Starting from a gentle introduction to the generative learning approaches, this book takes you through different variants of GANs, explaining some key technical and intuitive aspects about them. This book provides hands-on examples of multiple GAN variants and also, explains different ways to evaluate them. It covers key applications of GANs and also, explains the adversarial examples. Table of Contents 1. Generative Learning 2. Generative Adversarial Networks 3. GAN Failure Modes 4. Deep Convolutional GANs 4(II). Into the Latent Space 5. Towards stable GANs 6. Conditional GANs 7. Better Loss functions 8. Image-to-Image Translation 9. Other GANs and experiments 9(II). Advanced Scaling of GANs 10. How to evaluate GANs? 11. Adversarial Examples 12. Impressive Applications of GANs 13. Top Research Papers

Download or read book Hands On Generative Adversarial Networks with Keras written by Rafael Valle and published by . This book was released on 2019 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 ...

Download or read book Generative Deep Learning written by David Foster and published by "O'Reilly Media, Inc.". This book was released on 2019-06-28 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Download or read book Generative Adversarial Networks in Practice written by Mehdi Ghayoumi and published by CRC Press. This book was released on 2023-12-20 with total page 665 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an all-inclusive resource that provides a solid foundation on Generative Adversarial Networks (GAN) methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts. Key Features: • Guides you through the complex world of GANs, demystifying their intricacies • Accompanies your learning journey with real-world examples and practical applications • Navigates the theory behind GANs, presenting it in an accessible and comprehensive way • Simplifies the implementation of GANs using popular deep learning platforms • Introduces various GAN architectures, giving readers a broad view of their applications • Nurture your knowledge of AI with our comprehensive yet accessible content • Practice your skills with numerous case studies and coding examples • Reviews advanced GANs, such as DCGAN, cGAN, and CycleGAN, with clear explanations and practical examples • Adapts to both beginners and experienced practitioners, with content organized to cater to varying levels of familiarity with GANs • Connects the dots between GAN theory and practice, providing a well-rounded understanding of the subject • Takes you through GAN applications across different data types, highlighting their versatility • Inspires the reader to explore beyond this book, fostering an environment conducive to independent learning and research • Closes the gap between complex GAN methodologies and their practical implementation, allowing readers to directly apply their knowledge • Empowers you with the skills and knowledge needed to confidently use GANs in your projects Prepare to deep dive into the captivating realm of GANs and experience the power of AI like never before with Generative Adversarial Networks (GANs) in Practice. This book brings together the theory and practical aspects of GANs in a cohesive and accessible manner, making it an essential resource for both beginners and experienced practitioners.