Deep Learning Foundations and Concepts

This book provides a thorough introduction to the fundamental principles of deep learning. It caters to both newcomers and experienced individuals in the realm of machine learning. By covering essential ideas related to cutting-edge architectures and techniques, this book lays a strong foundation for readers to potentially specialize in the future. As the field of deep learning is rapidly evolving, the focus of this book is on enduring concepts that stand the test of time.

Organized into concise chapters, each dedicated to a distinct topic, the book offers a linear progression that builds upon previous content. This structure is ideal for teaching a two-semester undergraduate or postgraduate course in machine learning, while also being valuable for researchers and self-learners. While a basic understanding of mathematics is necessary for machine learning, the book includes a self-contained introduction to probability theory. However, the primary focus remains on practical applications rather than abstract theories.

Chris Bishop, a Technical Fellow at Microsoft and Director of Microsoft Research AI4Science, has substantial expertise in neural networks and deep learning. A Fellow of prestigious institutions such as Darwin College Cambridge, the Royal Academy of Engineering, and the Royal Society, Bishop brings a wealth of knowledge to this book. Hugh Bishop, an Applied Scientist at Wayve in London, specializes in designing and training deep neural networks, with a background in machine learning from Cambridge University.

Praised by experts in the field, including Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, this book is considered a valuable resource for understanding modern deep learning concepts and their practical applications. With clear explanations, comprehensive coverage of algorithms, and a focus on real-world relevance, this book serves as an essential guide for anyone interested in delving into the realm of deep learning and neural networks.

product information: