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Information Theory, Inference and Learning Algorithms
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This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction.
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Detalji o proizvodu
- Unites information theory and inference in an entertaining textbook
- Applicable to communication, signal processing, data mining, and more
- Introduces theory alongside practical communication systems and tools
- Covers state-of-the-art error-correcting codes for various applications
- Richly illustrated with over 400 exercises and detailed solutions
- Ideal for self-learning, undergraduate or graduate courses, and professionals
| Publisher | Cambridge University Press |
| Publication date | September 25, 2003 |
| Edition | Illustrated |
| Language | English |
| Print length | 642 pages |
| ISBN-10 | 0521642981 |
| ISBN-13 | 978-0521642989 |
| Item Weight | 3.36 pounds (1.52 kg) |
| Dimensions | 7 x 1.45 x 10 inches (17.8 x 3.7 x 25.4 cm) |
Who Should Buy?
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Students of Data Science
Ideal for students looking to understand the foundations of information theory and machine learning applications.
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Academics and Researchers
Useful for researchers needing a comprehensive reference in information theory and its algorithmic applications.
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Professionals in AI
Beneficial for professionals working in artificial intelligence who require insights into inference and learning algorithms.
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Casual Readers
Not suitable for casual readers without a strong background in mathematics or computer science.
OPIS PROIZVODA
Information Theory, Inference and Learning Algorithms
Pitanja i odgovori kupaca
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pitanje:
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Neural Networks Editorial Review
Information Theory, Inference and Learning Algorithms offers a profound exploration of the intersections of various disciplines, making it an insightful read for advanced students in fields like ECE. With 642 pages of well-illustrated content, it serves both as an introductory text for newcomers and a deeper analysis for those already familiar with information theory. Readers appreciate the book's engaging narrative, reminiscent of a teacher's guidance, even as some mention that certain concepts lack thorough explanations. The book is particularly notable for its coverage of topics such as the Bayesian framework and neural networks, enhancing its appeal to those interested in the practical applications of theoretical concepts.
Customer Reviews & Ratings
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Pros
- Engaging narrative resembles a teacher's guidance
- Well-illustrated with beautiful insights
- Covers multi-disciplinary connections
- Valuable for self-study with solution sections
- Great for advanced studies in ECE
Protiv
- Text size is small and hard to read
Product Price History
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Značajke i prednosti
- Information theory and inference taught together
- Applications used to illustrate the theory
- Inference techniques developed alongside application examples
- Richly illustrated with examples and exercises
- Suitable for self-learning or as a course textbook
- Ideal for those in diverse fields like biology, finance, and machine learning
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