Mathematical Foundations of Deep Learning

Theory and Algorithms

AvXiaojing Ye

Inbunden, Engelska, 2026

Del i serien Chapman & Hall/CRC Mathematics and Artificial Intelligence Series

2 330 kr

Kommande

Fler format och utgåvor

Häftad

Kommande

Beskrivning

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, the theory and algorithms of optimal control 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.

Produktinformation

Utgivningsdatum:2026-08-25
Mått:178 x 254 x undefined mm
Format:Inbunden
Språk:Engelska
Serie:Chapman & Hall/CRC Mathematics and Artificial Intelligence Series
Antal sidor:288
Förlag:Taylor & Francis Ltd
ISBN:9781032875507

Utforska kategorier

Mer om författaren

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 for scientific computing. His work also spans network science, numerical optimization, image processing, and related interdisciplinary areas.Dr. Ye began his undergraduate studies at Peking University in China in 2001, initially majoring in chemistry. Motivated by a growing interest in mathematics and physics, he transferred to the mathematics major in 2002 while pursuing physics as a minor. He received his Bachelor’s degree in Mathematics major in July 2005. After one year of professional experience, he returned to academia to pursue graduate studies at the University of Florida in the USA, supported by a prestigious four-year university alumni fellowship. Dr. Ye earned a Master’s degree in Statistics in 2009 and completed his Ph.D. in Mathematics in May 2011. He subsequently served as a Visiting Assistant Professor in the School of Mathematics at the Georgia Institute of Technology for two years. In 2013, he joined the Department of Mathematics and Statistics at Georgia State University as a tenure-track Assistant Professor, where he was awarded tenure and later promoted to Full Professor.