Автор: Mukhopadhyay Название: Deep Generative Models ISBN: 3031185757 ISBN-13(EAN): 9783031185755 Издательство: Springer Рейтинг: Цена: 7685.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the Second MICCAI Workshop on Deep Generative Models, DG4MICCAI 2022, held in conjunction with MICCAI 2022, in September 2022. The workshops took place in Singapore. DG4MICCAI 2022 accepted 12 papers from the 15 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community.
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: Hany John, Walters Greg Название: Hands-On Generative Adversarial Networks with PyTorch 1.x ISBN: 1789530512 ISBN-13(EAN): 9781789530513 Издательство: Неизвестно Рейтинг: Цена: 8091.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will help you understand how GANs architecture works using PyTorch. You will get familiar with the most flexible deep learning toolkit and use it to transform ideas into actual working codes. You will apply GAN models to areas like computer vision, multimedia and natural language processing using a sample-generation perspective.
Artifi cial Intelligence (AI) has been an exciting fi eld of study and research in educational institutions and research labs across the globe. Technology giants and IT organizations invest heavily on AI technologies and tools with the aim of preciselyautomating a variety of simple as well as complicated business operations acrossindustry verticals. This book covers the latest trends and transitions happening in thefuturistic AI domain. The book also focuses on machine and deep learning (ML/DL)algorithms, which are, undoubtedly, the mainstream implementation technologies ofstate-of-the-art AI systems and services. Also, there are chapters on computer vision(CV) and natural language processing (NLP), the primary use cases and applicationsof AI. The book has well-written chapters for demystifying AI model engineeringmethods. Further on, our esteemed readers can fi nd details on AI model evaluation,optimization, deployment and observability. Finally, the book deals and describesgenerative AI, the latest buzzword in the IT industry.
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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.
Автор: Tomczak Название: Deep Generative Modeling ISBN: 3030931609 ISBN-13(EAN): 9783030931605 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика 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.
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.
Описание: Generative adversarial networks (GANs) were introduced by Ian Goodfellow and his co-authors including Yoshua Bengio in 2014, and were to referred by Yann Lecun (Facebook’s AI research director) as “the most interesting idea in the last 10 years in ML.” GANs’ potential is huge, because they can learn to mimic any distribution of data, which means they can be taught to create worlds similar to our own in any domain: images, music, speech, prose. They are robot artists in a sense, and their output is remarkable – poignant even. In 2018, Christie’s sold a portrait that had been generated by a GAN for $432,000. Although image generation has been challenging, GAN image generation has proved to be very successful and impressive. However, there are two remaining challenges for GAN image generation: the quality of the generated image and the training stability. This book first provides an overview of GANs, and then discusses the task of image generation and the details of GAN image generation. It also investigates a number of approaches to address the two remaining challenges for GAN image generation. Additionally, it explores three promising applications of GANs, including image-to-image translation, unsupervised domain adaptation and GANs for security. This book appeals to students and researchers who are interested in GANs, image generation and general machine learning and computer vision.
Автор: By Mehdi Ghayoumi Название: Generative Adversarial Networks in Practice ISBN: 1032248440 ISBN-13(EAN): 9781032248448 Издательство: Taylor&Francis Рейтинг: Цена: 12554.00 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.
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.
Автор: Mao Xudong, Li Qing Название: Generative Adversarial Networks for Image Generation ISBN: 981336047X ISBN-13(EAN): 9789813360471 Издательство: Springer Цена: 20962.00 р. Наличие на складе: Поставка под заказ.
Описание: This book is intended for periodontal residents and practicing periodontists who wish to incorporate the principles of moderate sedation into daily practice. Comprehensive airway management and rescue skills are then documented in detail so that the patient may be properly managed in the event that the sedation progresses beyond the intended level.
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