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Lingua: Inglese
Editore: Springer-Nature New York Inc, 2024
ISBN 10: 3031106040 ISBN 13: 9783031106040
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Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook offers a comprehensive introduction to deep learning and neural networks, integrating core foundations with the latest advances. It begins with essential machine learning concepts and classic neural network architectures before progressing through convolutional models, backpropagation, regularization, generalization theory, PAC learning, and Boltzmann machines. Advanced chapters cover sequence models including recurrent networks, LSTMs, attention, Transformers, state-space models, and large language models alongside deep generative approaches such as VAEs, GANs, and diffusion models. Emerging topics include graph neural networks, self-supervised learning, metric learning, reinforcement learning, meta-learning, model compression, and knowledge distillation.Balancing mathematical rigor with hands-on practice, Elements of Deep Learning emphasizes both theoretical depth and real-world application. Different theories are introduced with PyTorch-based code examples, helping readers to translate theory into implementation. Organized into five sections fundamentals, sequence models, generative models, emerging topics, and practice the text provides a unified roadmap for mastering modern deep learning.Designed for advanced undergraduates, graduate students, instructors, and professionals in engineering, computer science, mathematics, and related fields, this book serves both as a primary course text and a reliable reference. With minimal prerequisites in linear algebra and calculus, it offers accessible explanations while equipping readers with practical tools for applications in vision, language, signal processing, healthcare, and beyond.
Lingua: Inglese
Editore: Springer, Berlin|Springer International Publishing|Springer, 2024
ISBN 10: 3031106040 ISBN 13: 9783031106040
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Aggiungi al carrelloKartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for better representation of data or separation between classes. This book presents a cohesive review of linear and .
Lingua: Inglese
Editore: Springer, Berlin|Springer International Publishing|Springer, 2022
ISBN 10: 3031106016 ISBN 13: 9783031106019
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Aggiungi al carrelloGebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for better representation of data or separation between classes. This book presents a cohesive review of linear and .
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Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This textbook offers a comprehensive introduction to deep learning and neural networks, integrating core foundations with the latest advances. It begins with essential machine learning concepts and classic neural network architectures before progressing through convolutional models, backpropagation, regularization, generalization theory, PAC learning, and Boltzmann machines. Advanced chapters cover sequence models including recurrent networks, LSTMs, attention, Transformers, state-space models, and large language models alongside deep generative approaches such as VAEs, GANs, and diffusion models. Emerging topics include graph neural networks, self-supervised learning, metric learning, reinforcement learning, meta-learning, model compression, and knowledge distillation.Balancing mathematical rigor with hands-on practice, Elements of Deep Learning emphasizes both theoretical depth and real-world application. Different theories are introduced with PyTorch-based code examples, helping readers to translate theory into implementation. Organized into five sectionsfundamentals, sequence models, generative models, emerging topics, and practicethe text provides a unified roadmap for mastering modern deep learning.Designed for advanced undergraduates, graduate students, instructors, and professionals in engineering, computer science, mathematics, and related fields, this book serves both as a primary course text and a reliable reference. With minimal prerequisites in linear algebra and calculus, it offers accessible explanations while equipping readers with practical tools for applications in vision, language, signal processing, healthcare, and beyond.Springer Nature Customer Service Center GmbH, Europaplatz 3, 69115 Heidelberg 596 pp. Englisch.