{"title":"Christopher M. Bishop","description":"Chris Bishop is a Technical Fellow at Microsoft and is the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College, Cambridge, a Fellow of the Royal Academy of Engineering, a Fellow of the Royal Society of Edinburgh, and a Fellow of the Royal Society of London. He is a keen advocate of public engagement in science, and in 2008 he delivered the prestigious Royal Institution Christmas Lectures, established in 1825 by Michael Faraday, and broadcast on prime-time national television. Chris was a founding member of the UK AI Council and was also appointed to the Prime Minister's Council for Science and Technology.Hugh Bishop is an Applied Scientist at Wayve, an end-to-end deep learning based autonomous driving company in London, where he designs and trains deep neural networks. Before working at Wayve, he completed his MPhil in Machine Learning and Machine Intelligence in the engineering department at Cambridge University. Hugh also holds an MEng in Computer Science from the University of Durham, where he focused his projects on deep learning. During his studies, he also worked as an intern at FiveAI, another autonomous driving company in the UK, and as a Research Assistant, producing educational interactive iPython notebooks for machine learning courses at Cambridge University.","products":[{"product_id":"deep-learning-foundations-and-concepts-hardcover","title":"Deep Learning: Foundations and Concepts - Hardcover","description":"\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time.\u003c\/p\u003e\u003cp\u003eThe book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eA full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eChris Bishop\u003c\/b\u003e is a Technical Fellow at Microsoft and is the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eHugh Bishop\u003c\/b\u003e is an Applied Scientist at Wayve, a deep learning autonomous driving company in London, where he designs and trains deep neural networks. He completed his MPhil in Machine Learning and Machine Intelligence at Cambridge University.\u003c\/p\u003e\u003cp\u003e\u003ci\u003e\"Chris Bishop wrote a terrific textbook on neural networks in 1995 and has a deep knowledge of the field and its core ideas. His many years of experience in explaining neural networks have made him extremely skillful at presenting complicated ideas in the simplest possible way and it is a delight to see these skills applied to the revolutionary new developments in the field.\"\u003c\/i\u003e -- \u003cb\u003eGeoffrey Hinton\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003e\u003cb\u003e\"\u003c\/b\u003eWith the recent explosion of deep learning and AI as a research topic, and the quickly growing importance of AI applications, a modern textbook on the topic was badly needed. The \"New Bishop\" masterfully fills the gap, covering algorithms for supervised and unsupervised learning, modern deep learning architecture families, as well as how to apply all of this to various application areas.\"\u003c\/i\u003e - \u003cb\u003eYann LeCun\u003c\/b\u003e\u003cb\u003e\u003cbr\u003e\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003e\"This excellent and very educational book will bring the reader up to date with the main concepts and advances in deep learning with a solid anchoring in probability. Theseconcepts are powering current industrial AI systems and are likely to form the basis of further advances towards artificial general intelligence.\"\u003c\/i\u003e -- \u003cb\u003eYoshua Bengio\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time.\u003c\/p\u003e\u003cp\u003eThe book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eA full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eChris Bishop\u003c\/b\u003e is a Technical Fellow at Microsoft and is the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society. \u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eHugh Bishop\u003c\/b\u003e is an Applied Scientist at Wayve, a deep learning autonomous driving company in London, where he designs and trains deep neural networks. He completed his MPhil in Machine Learning and Machine Intelligence at Cambridge University.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003e\"Chris Bishop wrote a terrific textbook on neural networks in 1995 and has a deep knowledge of the field and its core ideas. His many years of experience in explaining neural networks have made him extremely skillful at presenting complicated ideas in the simplest possible way and it is a delight to see these skills applied to the revolutionary new developments in the field.\"\u003c\/i\u003e -- \u003cb\u003eGeoffrey Hinton\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003e\u003cb\u003e\"\u003c\/b\u003eWith the recent explosion of deep learning and AI as a research topic, and the quickly growing importance of AI applications, a modern textbook on the topic was badly needed. The \"New Bishop\" masterfully fills the gap, covering algorithms for supervised and unsupervised learning, modern deep learning architecture families, as well as how to apply all of this to various application areas.\"\u003c\/i\u003e - \u003cb\u003eYann LeCun\u003c\/b\u003e\u003cb\u003e\u003cbr\u003e\u003c\/b\u003e\u003c\/p\u003e\u003ci\u003e\"This excellent and very educational book will bring the reader up to date with the main concepts and advances in deep learning with a solid anchoring inprobability. These concepts are powering current industrial AI systems and are likely to form the basis of further advances towards artificial general intelligence.\"\u003c\/i\u003e -- \u003cb\u003eYoshua Bengio\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":51319060234463,"sku":"9783031454677","price":145.78,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0813\/8958\/4607\/files\/QdXp39LCiT9783031454677.webp?v=1788366140"},{"product_id":"pattern-recognition-and-machine-learning-paperback","title":"Pattern Recognition and Machine Learning - Paperback","description":"\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThe dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.\u003c\/p\u003e \u003cp\u003eThis completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.\u003c\/p\u003e \u003cp\u003eThe 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. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.\u003c\/p\u003e \u003cp\u003eChristopher M. Bishop is Deputy Director of Microsoft Research Cambridge, and holds a Chair inComputer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society of Edinburgh. His previous textbook \"Neural Networks for Pattern Recognition\" has been widely adopted.\u003c\/p\u003e \u003cp\u003eComing soon: \u003c\/p\u003e \u003cp\u003e*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked \"www\" in the text)\u003c\/p\u003e \u003cp\u003e*For instructors, worked solutions to remaining exercises from the Springer web site\u003c\/p\u003e \u003cp\u003e*Lecture slides to accompany each chapter\u003c\/p\u003e \u003cp\u003e*Data sets available for download\u003cbr\u003e\u003c\/p\u003e","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":51319079207135,"sku":"9781493938438","price":137.68,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0813\/8958\/4607\/files\/7cx28iHFnc9781493938438.webp?v=1788366172"}],"url":"https:\/\/blackandbarhe.com\/collections\/christopher-m-bishop.oembed","provider":"Black \u0026 Barhe Bookstore","version":"1.0","type":"link"}