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Item #:
41593414
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series)
88% of respondents would recommend this to a friend
Item #:
41593414
MNT 239269
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What Stands Out
Comprehensive Coverage
This book provides an extensive overview of Gaussian processes, making complex concepts accessible for practitioners and researchers, ultimately enhancing users' understanding of machine learning applications.
Practical Applications
Real-world examples illustrate how Gaussian processes can be applied, showcasing their versatility in solving diverse problems in machine learning, which sets it apart from theoretical-focused resources.
Expert Insights
Authored by leading experts in the field, the book includes valuable insights and methodologies, ensuring readers gain cutting-edge knowledge and practical skills that add immense value to their professional toolkit.
Product Details
| Publisher | The MIT Press |
| Publication date | November 23, 2005 |
| Language | English |
| Print length | 272 pages |
| ISBN-10 | 9780262182539 |
| ISBN-13 | 978-0262182539 |
| Item Weight | 1.62 pounds (730 grams) |
| Reading age | 18 years and up |
| Dimensions | 10.22 x 8.26 x 0.73 inches (26 x 21 x 1.9 cm) |
| Grade level | 12 and up |
Who Should Buy?
-
Data Scientists
Ideal for data scientists seeking to leverage advanced probabilistic modeling techniques for real-world applications.
-
Research Academics
A valuable resource for academics researching machine learning, offering theoretical foundations and practical insights into Gaussian processes.
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Graduate Students
Perfect for graduate students studying machine learning, providing comprehensive content that's both theoretical and practical.
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Beginners
Not suitable for beginners due to its complex mathematical content and prerequisite knowledge of machine learning concepts.
Product Description
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series)
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Intelligence & Semantics Editorial Review
This book on Gaussian Processes (GP) is highly recommended for academic researchers working on GP related projects. However, the book is not just about GP, it also includes a lot of general Machine Learning (ML) details. The book is technical but also does a great job explaining how GP fits into the big picture of the last few decades in the ML field and how it relates to Support Vector Machines (SVM) and Neural Networks. The explanations are clear and to the point. Although it is not a cookbook on GP, it is a valuable reference. The book is well-edited and comes in hardcover - a great price for what it offers.
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Pros
- Valuable reference for GP related research
- Includes general ML details
- Clear and concise explanations
- Well-edited hardcover book
- Affordable price
Cons
- Highly technical, may not be suitable for beginners
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