Christopher M. Bishop Reihenfolge der Bücher (Chronologisch)

This book provides a thorough introduction to the fundamental concepts of deep learning, catering to both newcomers and seasoned professionals in machine learning. It addresses key ideas related to contemporary architectures and techniques, establishing a strong foundation for future specialization. As the field rapidly evolves, the focus is on enduring concepts. Organized into concise chapters, each one builds on the previous, making it ideal for a two-semester undergraduate or postgraduate course, while also being suitable for researchers and self-learners. A mathematical background is necessary, so the book includes a self-contained introduction to probability theory. However, it emphasizes clarity in understanding, prioritizing practical applications over abstract theory. Complex ideas are presented through various perspectives, including text, diagrams, mathematical formulas, and pseudo-code. Chris Bishop, a Technical Fellow at Microsoft and an esteemed figure in the field, along with Hugh Bishop, an Applied Scientist at Wayve, contribute their expertise. The book has been praised for its clarity and relevance, addressing the urgent need for a modern textbook in deep learning and AI, and anchoring its concepts in probability to reflect current industrial AI systems and future advancements toward artificial general intelligence.

This is the first text on pattern recognition to present the Bayesian viewpoint, one that has become increasing popular in the last five years. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It provides the first text to use graphical models to describe probability distributions when there are no other books that apply graphical models to machine learning. It is also the first four-color book on pattern recognition. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher.

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In recent years neural computing has emerged as a practical technology, with successful applications in many fields. The majority of these applications are concerned with problems in pattern recognition, and make use of feedforward network architectures such as the multilayer perceptron and the radial basis function network. Also, it has become widely acknowledged that successful applications of neural computing require a principled, rather than ad hoc, approach. (From the preface to „Neural Networks for Pattern Recognition“ by C. M. Bishop, Oxford Univ Press 1995.) This NATO volume, based on a 1997 workshop, presents a coordinated series of tutorial articles covering recent developments in the field of neural computing. It is ideally suited to graduate students and researchers.