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 Intelligent Data Engineering and Automated Learning IDEAL 2020 written by Cesar Analide and published by Springer Nature. This book was released on 2020-10-29 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set of LNCS 12489 and 12490 constitutes the thoroughly refereed conference proceedings of the 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020, held in Guimaraes, Portugal, in November 2020.* The 93 papers presented were carefully reviewed and selected from 134 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2020 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspiredmodels, agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI. * The conference was held virtually due to the COVID-19 pandemic.

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 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 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 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 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.

Download or read book Generative Adversarial Networks for Image to Image Translation written by Arun Solanki and published by Academic Press. This book was released on 2021-06-22 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative Adversarial Networks (GAN) have started a revolution in Deep Learning, and today GAN is one of the most researched topics in Artificial Intelligence. Generative Adversarial Networks for Image-to-Image Translation provides a comprehensive overview of the GAN (Generative Adversarial Network) concept starting from the original GAN network to various GAN-based systems such as Deep Convolutional GANs (DCGANs), Conditional GANs (cGANs), StackGAN, Wasserstein GANs (WGAN), cyclical GANs, and many more. The book also provides readers with detailed real-world applications and common projects built using the GAN system with respective Python code. A typical GAN system consists of two neural networks, i.e., generator and discriminator. Both of these networks contest with each other, similar to game theory. The generator is responsible for generating quality images that should resemble ground truth, and the discriminator is accountable for identifying whether the generated image is a real image or a fake image generated by the generator. Being one of the unsupervised learning-based architectures, GAN is a preferred method in cases where labeled data is not available. GAN can generate high-quality images, images of human faces developed from several sketches, convert images from one domain to another, enhance images, combine an image with the style of another image, change the appearance of a human face image to show the effects in the progression of aging, generate images from text, and many more applications. GAN is helpful in generating output very close to the output generated by humans in a fraction of second, and it can efficiently produce high-quality music, speech, and images. - Introduces the concept of Generative Adversarial Networks (GAN), including the basics of Generative Modelling, Deep Learning, Autoencoders, and advanced topics in GAN - Demonstrates GANs for a wide variety of applications, including image generation, Big Data and data analytics, cloud computing, digital transformation, E-Commerce, and Artistic Neural Networks - Includes a wide variety of biomedical and scientific applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing, and disease diagnosis - Provides a robust set of methods that will help readers to appropriately and judiciously use the suitable GANs for their applications

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 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 Proceedings of International Scientific Conference on Telecommunications Computing and Control written by Nikita Voinov and published by Springer Nature. This book was released on 2021-04-28 with total page 541 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a platform for academics and practitioners for sharing innovative results, approaches, developments, and research projects in computer science and information technology, focusing on the latest challenges in advanced computing and solutions introducing mathematical and engineering approaches. The book presents discussions in the area of advances and challenges of modern computer science, including telecommunications and signal processing, machine learning and artificial intelligence, intelligent control systems, modeling and simulation, data science and big data, data visualization and graphics systems, distributed, cloud and high-performance computing, and software engineering. The papers included are presented at TELECCON 2019 organized by Peter the Great St. Petersburg University during November 18–19, 2019.

Download or read book Generative Adversarial Networks for Extrapolation of Corrosion in Automobile Images written by Andre Von Zuben and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has shown success in several applications involving pattern recognition, expert systems, and scientific discovery. However, existing methods struggle with industrial applications, which are often challenged by non-ideal datasets. In many cases, the datasets are small, poorly labeled, noisy, or have unbalanced class distribution or any combination of such problems. In this Master's research, we propose a generative adversarial network (GAN) strategy that is able to circumvent limitations imposed by tiny datasets. As a case study, we use the extrapolation of corrosion in automobiles and feed our deep learning framework with only a few dozen images instead of the thousands to million images commonly found in many computer vision problems. In order to handle such a reduced dataset, we use one GAN for the rust level and one for the rust texture. The rust level GAN is conditional on random samples from the dataset and uses an additive random noise in the latent space to add variability to the generated rust level maps. The rust texture GAN adds shades of brown to the outputs of the rust level GAN. Loss functions are carefully designed to produce a robust training scheme for both GANs. In addition, given the significantly reduced size of our dataset, it is unfeasible to break down the data into training, validation, and test sets. We overcome this limitation by using the discrepancy between the generated and target distributions of the rust level and texture intensities as a way to monitor the convergence of training. The resulting models can ingest an image with a car having no corrosion and generate an image of this car with parts exhibiting varying degrees of corrosion (from mild to moderate to severe).

Download or read book Enhancing Security in Public Spaces Through Generative Adversarial Networks GANs written by Ponnusamy, Sivaram and published by IGI Global. This book was released on 2024-05-16 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the demand for data security intensifies, the vulnerabilities become glaring, exposing sensitive information to potential threats. In this tumultuous landscape, Generative Adversarial Networks (GANs) emerge as a groundbreaking solution, transcending their initial role as image generators to become indispensable guardians of data security. Within the pages of Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs), readers are guided through the intricate world of GANs, unraveling their unique design and dynamic adversarial training. The book presents GANs not merely as a technical marvel but as a strategic asset for organizations, offering a comprehensive solution to fortify cybersecurity, protect data privacy, and mitigate the risks associated with evolving cyber threats. It navigates the ethical considerations surrounding GANs, emphasizing the delicate balance between technological advancement and responsible use.

Download or read book Healthcare Industry Assessment Analyzing Risks Security and Reliability written by Pardeep Kumar and published by Springer Nature. This book was released on with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Mastering Generative Adversarial Networks From Basics to Advanced Applications written by Nagaram Ramesh and published by SGSH Publications. This book was released on 2024-08-11 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Mastering Generative Adversarial Networks From Basics to Advanced Applications offers a thorough examination of Generative Adversarial Networks (GANs), covering their theoretical foundations, architectural innovations, and practical applications. This book delves into the original GAN model, its evolution, and the mathematical frameworks that support it, while also exploring cutting-edge variants and real-world use cases. Authored by Nagaram Ramesh and V. Bhargavi, it provides a crucial resource for academics, researchers, and practitioners aiming to deepen their understanding and effectively implement GAN technologies.

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 Network Based Machine for Fake Data Generation written by Esteban Piacentino and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper introduces a first approach on using Generative Adversarial Networks (GANs) for the generation of fake data, with the objective of anonymizing patients information in the health sector. This is intended to create valuable data that can be used both, in educational and research areas, while avoiding the risk of a sensitive data leakage. For this purpose, firstly a thorough research on GAN's state of the art and available databases has been developed. The outcome of the project is a GAN system prototype adapted to generate raw data that imitates samples such as users variable status on hypothyroidism or a cardiogram report. The performance of this prototype has been checked and satisfactory results have been obtained for this first phase. Moreover, a novel research pathway has been opened so further research can be developed.