Deep Learning: Foundations and Concepts aims to offer both newcomers to machine learning and those already experienced in the field a comprehensive grasp of fundamental ideas underpinning deep learning. Covering key concepts related to contemporary deep...
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to...
This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probabil...