Deep Learning: Foundations and Concepts

This book provides a comprehensive introduction to deep learning, covering key concepts, architectures, and techniques. It is intended for newcomers to machine learning and for those already experienced in the field. The book is organized into bite-sized chapters and includes a self-contained introduction to probability theory. The focus is on conveying a clear understanding of ideas with emphasis on practical real-world applications. The authors are highly experienced in the field and have written a textbook that is both educational and accessible.

Format: Unspecified
Length: 649 pages
Publication date: 02 November 2023
Publisher: Springer International Publishing AG

This comprehensive book provides a thorough introduction to the fundamental principles that underpin deep learning. It caters to both novice machine learning enthusiasts and seasoned professionals in the field. Covering key concepts related to contemporary architectures and techniques, this essential resource equips readers with a solid foundation for potential future specialization.

The field of deep learning is experiencing a remarkable surge of growth, and this book specifically emphasizes ideas that are poised to stand the test of time. The book is meticulously organized into numerous concise chapters, each delving into a distinct topic. The narrative follows a linear progression, with each chapter building upon the content of its predecessors. This structure is ideally suited for teaching a two-semester undergraduate or postgraduate machine learning course, while also remaining relevant to individuals engaged in active research or self-study.

A thorough grasp of machine learning necessitates a solid foundation in mathematics, and this book includes a self-contained introduction to probability theory. However, the primary focus of the book is on conveying a clear understanding of concepts, with a strong emphasis on the practical applications of techniques rather than on abstract theory. Complex concepts are therefore presented from various complementary perspectives, including textual descriptions, diagrams, mathematical formulations, and pseudo-code.

Chris Bishop, a Technical Fellow at Microsoft and the Director of Microsoft Research AI4Science, holds prestigious fellowships from institutions such as Darwin College Cambridge, the Royal Academy of Engineering, and the Royal Society. Hugh Bishop, an Applied Scientist at Wayve, a deep learning autonomous driving company in London, is responsible for designing and training deep neural networks.

Weight: 1444g
Dimension: 254 x 178 (mm)
ISBN-13: 9783031454677
Edition number: 1st ed. 2024