Автор: Edited By Roshani Raut, Pranav D Pathak, Sachin R Название: Generative Adversarial Networks and Deep Learning Theory and Applications ISBN: 1032068108 ISBN-13(EAN): 9781032068107 Издательство: Taylor&Francis Рейтинг: Цена: 22968.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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 GANHighlights the inclusion of gaming effects using deep learning methodsExamines the significant technological advancements in GAN and its real-world application. Discusses as GAN challenges and optimal solutionsThe 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
Описание: Rearview Funhouse is a collection of poems that seeks to put the spectacle of loss and what comes after on vibrant and aching display, asking the reader to consider -or reconsider - the ways that we collectively process loss and the expectations we place on the grieving.
Автор: Parra Pennefather Название: Creative Prototyping with Generative AI ISBN: 1484295781 ISBN-13(EAN): 9781484295786 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Reimagine different generative AI as useful creative prototyping tools that can be used to augment your own creative process and projects. Gain a deeper understanding of how generative AI can elevate your creative future. You will acquire a comprehensive understanding of how AI works, uncover tools that can enhance your AI interactions, learn how to extract maximum potential from AI-produced content, and experiment with methods for assessing, refining, and boosting the content to transform your creative projects. You'll also explore how creative professionals from varied disciplines are employing generative AI in their workflows to produce distinctive contributions to the world. Each chapter provides examples of how designers and other creative individuals can utilize these technological wonders, adopting various prototyping techniques to fast-track and optimize design processes and workflows. Creators from all disciplines can tap into the vast capabilities and benefits of generative AI, enabling them to rapidly experiment and prototype their ideas. You Will Learn: * Understand how generative AI can support your own creative process * Learn tools to get the most out of text-text, text-image, and text-video generative AI * Augment your design practices using generative AI * Draw inspiration from AI generated content to create unique creative work * Improve and streamline creatives processes and workflows Who This Book Is For * Digital media professionals who want to access off-the shelf creative tools to improve and accelerate their creativity and workflow. * Designers and engineers who are looking at novel ways to improve their prototyping and testing processes. * Students who want to use AI to rapidly generate ideas to support them in prototyping assignments. * Instructors interested in pointing their students to a variety of accessible AI resources to manage their own creativity.
Описание: This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021.
Автор: Taulli Название: Generative AI ISBN: 148429369X ISBN-13(EAN): 9781484293690 Издательство: Springer Рейтинг: Цена: 7685.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will show how generative technology works and the drivers. It will also look at the applications – showing what various startups and large companies are doing in the space. There will also be a look at the challenges and risk factors. During the past decade, companies have spent billions on AI. But the focus has been on applying the technology to predictions – which is known as analytical AI. It can mean that you receive TikTok videos that you cannot resist. Or analytical AI can fend against spam or fraud or forecast when a package will be delivered. While such things are beneficial, there is much more to AI. The next megatrend will be leveraging the technology to be creative. For example, you could take a book and an AI model will turn it into a movie – at very little cost. This is all part of generative AI. It’s still in the nascent stages but it is progressing quickly. Generative AI can already create engaging blog posts, social media messages, beautiful artwork and compelling videos. The potential for this technology is enormous. It will be useful for many categories like sales, marketing, legal, product design, code generation, and even pharmaceutical creation. What You Will Learn The importance of understanding generative AI The fundamentals of the technology, like the foundation and diffusion models How generative AI apps work How generative AI will impact various categories like the law, marketing/sales, gaming, product development, and code generation. The risks, downsides and challenges. Who This Book is For Professionals that do not have a technical background. Rather, the audience will be mostly those in Corporate America (such as managers) as well as people in tech startups, who will need an understanding of generative AI to evaluate the solutions.
Автор: Jayne Osgood, Kerry H. Robinson Название: Feminists Researching Gendered Childhoods: Generative Entanglements ISBN: 1350178985 ISBN-13(EAN): 9781350178984 Издательство: Bloomsbury Academic Рейтинг: Цена: 5226.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Feminists Researching Gendered Childhoods charts the evolving nature of feminist theory and research methods in childhood studies and the generative potential this holds for researchers, academics and educators to continue to push ideas and practices. The book traces the threads of affect and effect that feminist theories and methodologies have made over time to thinking more, and differently, about gender in childhood. In the wake of the ‘new materialist turn’ in feminist research, the book sought to address two pressing questions: what is especially new about feminist new materialism, and what is especially feminist about feminist new materialism. These questions are generative, troubling, unsettling and invited the contributors on an adventure that involved re-turning and reconfiguring ideas and practices about gender and childhood. Along with the editors, Jayne Osgood (UK), and Kerry H. Robinson (Australia), five key international feminist scholars, Mindy Blaise (Australia), Bronwyn Davies (Australia), Debbie Epstein (UK), Jen Lyttleton-Smith (UK), and Veronica Pacini-Ketchabaw (Canada) collaborated on this book project. Their reflective accounts capture the contribution of their own work and that of their peers, to advancing research practices and theorisations of gender in childhood. Having all approached the study of gendered childhoods in creative and critical ways, these important feminist researchers re-engage and critically reflect on their earlier work alongside their more contemporary contributions to the field. The book is as much about the processes involved in its creation as it about the material/digital end product. The chapters work with both familiar and unfamiliar feminist methodological frameworks that bring affect, materiality and embodiment, as well as textual representations of gender and childhood, into play. The book engages with, and generates artwork, poetry, photographs as a means to grapple with how gender, childhood, family, curriculum and policy have been, and might be researched. The book captures a lively, collaborative, feminist experiment that sought to make space for fresh conceptualisations of gender in childhood. Issues addressed include: social justice and transformative methodologies in childhood research; advancing theoretical perspectives that contribute to fresh understandings of gender in young children’s lives; the ways that research into gender in childhood play out in educational agendas; and the specific gender issues perceived critical to address in contemporary childhoods lived in the post-Anthropocene.
Автор: Granville,Vincent Название: Synthetic Data And Generative Ai ISBN: 0443218579 ISBN-13(EAN): 9780443218576 Издательство: Elsevier Science Рейтинг: Цена: 20380.00 р. Наличие на складе: Поставка под заказ.
Автор: Razavi-Far Roozbeh, Ruiz-Garcia Ariel, Palade Vasile Название: Generative Adversarial Learning: Architectures and Applications ISBN: 3030913899 ISBN-13(EAN): 9783030913892 Издательство: Springer Рейтинг: Цена: 25155.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Описание: Packed with intriguing real-world projects as well as theory, Generative AI with Python and TensorFlow 2 enables you to leverage artificial intelligence creatively and generate human-like data in the form of speech, text, images, and music.
Название: Generative AI in Higher Education ISBN: 1032604182 ISBN-13(EAN): 9781032604183 Издательство: Taylor&Francis Рейтинг: Цена: 22202.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Kulkarni Название: Applied Generative AI for Beginners ISBN: 1484299930 ISBN-13(EAN): 9781484299937 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a deep dive into the world of generative AI, covering everything from the basics of neural networks to the intricacies of large language models like ChatGPT and Google Bard. It serves as a one-stop resource for anyone interested in understanding and applying this transformative technology and is particularly aimed at those just getting started with generative AI. Applied Generative AI for Beginners is structured around detailed chapters that will guide you from foundational knowledge to practical implementation. It starts with an introduction to generative AI and its current landscape, followed by an exploration of how the evolution of neural networks led to the development of large language models. The book then delves into specific architectures like ChatGPT and Google Bard, offering hands-on demonstrations for implementation using tools like Sklearn. You’ll also gain insight into the strategic aspects of implementing generative AI in an enterprise setting, with the authors covering crucial topics such as LLMOps, technology stack selection, and in-context learning. The latter part of the book explores generative AI for images and provides industry-specific use cases, making it a comprehensive guide for practical application in various domains. Whether you're a data scientist looking to implement advanced models, a business leader aiming to leverage AI for enterprise growth, or an academic interested in cutting-edge advancements, this book offers a concise yet thorough guide to mastering generative AI, balancing theoretical knowledge with practical insights. What You Will Learn * Gain a solid understanding of generative AI, starting from the basics of neural networks and progressing to complex architectures like ChatGPT and Google Bard * Implement large language models using Sklearn, complete with code examples and best practices for real-world application * Learn how to integrate LLM’s in enterprises, including aspects like LLMOps and technology stack selection * Understand how generative AI can be applied across various industries, from healthcare and marketing to legal compliance through detailed use cases and actionable insights Who This Book Is For Data scientists, AI practitioners, Researchers and software engineers interested in generative AI and LLMs.
Автор: Tomczak Jakub M. Название: Deep Generative Modeling ISBN: 3030931579 ISBN-13(EAN): 9783030931575 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Поставка под заказ.
Описание: This textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github. The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.
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