9781032875507 - mathematical foundations of deep learning: theory and algorithms di ye, xiaojing (10 risultati)
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Condizione: New. Dr. Xiaojing Ye is a Professor of Mathematics at Georgia State University in Atlanta, USA. His research interests lie in applied and computational mathematics, with a particular focus on numerical methods that integrate deep learning techniques fo.
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Buch. Condizione: Neu. Neuware - Mathematical Foundations of Deep Learning offers a comprehensive and rigorous treatment of the mathematical principles underlying modern deep learning. The book spans core theoretical topics, from the approximation capabilities of deep neural networks and the theory and algorithms of optimal cont…rol and reinforcement learning integrated with deep learning techniques to contemporary generative models that drive today's advances in artificial intelligence.Designed as both a textbook for graduate and advanced undergraduate students as well as a long-term reference, this volume aims to equip students with a solid mathematical understanding of deep learning while serving researchers, scientists, and engineers seeking a principled framework for developing and analyzing modern artificial intelligence systems.Features - Comprehensive and rigorous, featuring detailed theoretical developments, mathematical proofs, and algorithmic frameworks throughout - Materials thoughtfully selected from this book support a full one-semester course for graduate students and advanced undergraduates - Concise yet precise exposition of core deep learning concepts and techniques, presented using exact and rigorous mathematical language.
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Hardcover. Condizione: new. Hardcover. Mathematical Foundations of Deep Learning offers a comprehensive and rigorous treatment of the mathematical principles underlying modern deep learning. The book spans core theoretical topics, from the approximation capabilities of deep neural networks and the theory and algorithms of optima…l control and reinforcement learning integrated with deep learning techniques to contemporary generative models that drive todays advances in artificial intelligence.Designed as both a textbook for graduate and advanced undergraduate students as well as a long-term reference, this volume aims to equip students with a solid mathematical understanding of deep learning while serving researchers, scientists, and engineers seeking a principled framework for developing and analyzing modern artificial intelligence systems.FeaturesComprehensive and rigorous, featuring detailed theoretical developments, mathematical proofs, and algorithmic frameworks throughoutMaterials thoughtfully selected from this book support a full one-semester course for graduate students and advanced undergraduatesConcise yet precise exposition of core deep learning concepts and techniques, presented using exact and rigorous mathematical language Offers a comprehensive and rigorous treatment of the mathematical principles underlying modern deep learning. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Hardcover. Condizione: new. Hardcover. Mathematical Foundations of Deep Learning offers a comprehensive and rigorous treatment of the mathematical principles underlying modern deep learning. The book spans core theoretical topics, from the approximation capabilities of deep neural networks and the theory and algorithms of optima…l control and reinforcement learning integrated with deep learning techniques to contemporary generative models that drive todays advances in artificial intelligence.Designed as both a textbook for graduate and advanced undergraduate students as well as a long-term reference, this volume aims to equip students with a solid mathematical understanding of deep learning while serving researchers, scientists, and engineers seeking a principled framework for developing and analyzing modern artificial intelligence systems.FeaturesComprehensive and rigorous, featuring detailed theoretical developments, mathematical proofs, and algorithmic frameworks throughoutMaterials thoughtfully selected from this book support a full one-semester course for graduate students and advanced undergraduatesConcise yet precise exposition of core deep learning concepts and techniques, presented using exact and rigorous mathematical language Offers a comprehensive and rigorous treatment of the mathematical principles underlying modern deep learning. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Hardcover. Condizione: new. Hardcover. Mathematical Foundations of Deep Learning offers a comprehensive and rigorous treatment of the mathematical principles underlying modern deep learning. The book spans core theoretical topics, from the approximation capabilities of deep neural networks and the theory and algorithms of optima…l control and reinforcement learning integrated with deep learning techniques to contemporary generative models that drive todays advances in artificial intelligence.Designed as both a textbook for graduate and advanced undergraduate students as well as a long-term reference, this volume aims to equip students with a solid mathematical understanding of deep learning while serving researchers, scientists, and engineers seeking a principled framework for developing and analyzing modern artificial intelligence systems.FeaturesComprehensive and rigorous, featuring detailed theoretical developments, mathematical proofs, and algorithmic frameworks throughoutMaterials thoughtfully selected from this book support a full one-semester course for graduate students and advanced undergraduatesConcise yet precise exposition of core deep learning concepts and techniques, presented using exact and rigorous mathematical language Offers a comprehensive and rigorous treatment of the mathematical principles underlying modern deep learning. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
