Pattern Recognition and Machine Learning

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Pattern Recognition and Machine LearningThis 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 describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with

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Focus on the expertise measured by these objectives: Manage Azure subscriptions and resources Implement and manage storage Deploy and manage virtual machines (VMs) Configure and manage virtual networks Manage identities This Microsoft Exam Ref: Organizes its coverage by exam objectives Features strategic

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General Considerations

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the subject is treated in more detail and the book is richly illustrated in the form of a picture gallery

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