Generative Adversarial Networks and Deep Learning Theory and Applications, Edited By Roshani Raut, Pranav D Pathak, Sachin R
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: 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: Partha Niyogi Название: The Informational Complexity of Learning ISBN: 1461374936 ISBN-13(EAN): 9781461374930 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Among other topics, The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar brings together two important but very different learning problems within the same analytical framework.
Описание: Provides a comprehensive survey of intelligent tools for analysis and design that take fundamental steps towards achieving resilient operation in Cyber-Physical Systems. The authors investigate the challenges of achieving reliable control and estimation over networks, particularly in the face of uncertainty and resource constraints.
Автор: Chen, Pin-yu (research Sta? Member, Ibm Thomas J. Watson Research Center, Yorktown Heights, Ny, Usa) Hsieh, Cho-jui (assistant Professor, Ucla Compute Название: Adversarial robustness for machine learning ISBN: 0128240202 ISBN-13(EAN): 9780128240205 Издательство: Elsevier Science Рейтинг: Цена: 14308.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: He is exactly what this age needs, a real voice of universal spirituality. His appeal is urgent, human and sacred. DAN CRUSEY Sidney, Ohio Each era has its own prophet poets. Russia has been praying and is praying poems by Pushkin, a holy name for every Russian. Ayaz seems to touch us with that sensitivity, inspiring and changing the rhythm of our breathing. SEBARITA KAKHOVSKAYA Ukraine His words transform you through a subtle Alchemy process, and you suddenly travel from a Neophyte to the Connoisseur of Mysteries. His poetry is a gateway to the stars ELLURA ZURIA Rhn, Germany Reading Ayaz is akin to a journey into the
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: 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.
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.
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.
Автор: Tony Jebara Название: Machine Learning ISBN: 1461347564 ISBN-13(EAN): 9781461347569 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Machine Learning: Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines.
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