Nº de artículo:
128878998
Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
87% of respondents would recommend this to a friend
Nº de artículo:
128878998
GTQ 1339
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This book offers a detailed and up-to-date introduction to machine learning through the unifying lens of probabilistic modeling and Bayesian decision theory.
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What Stands Out
Comprehensive Coverage
This book offers a thorough exploration of probabilistic machine learning, making complex concepts accessible for both newcomers and seasoned practitioners in the field.
Adaptive Computation Series
As part of the respected Adaptive Computation and Machine Learning series, this title ensures authoritative content that is up-to-date with the latest research and methodologies.
Practical Applications
With real-world examples and applications, this guide bridges theory and practice, enabling readers to apply probabilistic methods effectively in their own projects.
Detalles de producto
- Comprehensive introduction to machine learning presented through the lens of probabilistic modeling and Bayesian decision theory
- Covers mathematical background, basic supervised learning, and advanced topics including transfer learning and unsupervised learning
- Includes end-of-chapter exercises for practical application
- Completely new book reflecting the latest developments in the field, particularly deep learning
- Accompanied by online Python code for reproducing figures and practical implementation
- Part of a series, with a sequel covering more advanced topics in the same probabilistic approach
| Publisher | The MIT Press |
| Publication date | 1 Mar. 2022 |
| Language | English |
| Print length | 944 pages |
| ISBN-10 | 0262046822 |
| ISBN-13 | 978-0262046824 |
| Dimensions | 21.3 x 3.3 x 23.5 cm |
Who Should Buy?
-
Aspiring Data Scientists
Perfect for beginners wanting foundational knowledge in probabilistic methods for machine learning applications.
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Machine Learning Researchers
Provides advanced insights and techniques for researchers working on algorithm development and statistical modeling.
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Graduate Students
Ideal for graduate-level courses focusing on machine learning and statistical inference in academic settings.
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Casual Readers
Not suitable for casual readers seeking light or general information about machine learning without in-depth technicalities.
DESCRIPCIÓN DEL PRODUCTO
Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
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English edition Kevin P. Murphy Format: Hardcover Editorial Review
Probabilistic Machine Learning: An Introduction (Adaptive Computation And Machine Learning Series) offers a comprehensive overview of the foundational principles and methods of probabilistic machine learning, making it an essential read for both students and professionals in the field. Published by The MIT Press, this extensive 944-page volume released on March 1, 2022, covers an expansive range of topics. Its in-depth content is suitable for those looking to enhance their knowledge of machine learning theories and techniques. The book has been praised for its clarity and thoroughness, providing readers with robust theoretical insights while maintaining accessibility.
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ventajas
- Comprehensive overview of probabilistic methods
- Ideal for students and professionals
- Extensive 944 pages of content
- Published by a reputable press
- Clear and thorough explanations
Contras
- Some technical concepts may require prior knowledge
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características y beneficios
- Comprehensive introduction to machine learning and deep learning.
- Focus on probabilistic modeling and Bayesian decision theory.
- Covers essential mathematical backgrounds, including linear algebra and optimization.
- Includes practical exercises at the end of each chapter for real-world application.
- Accompanied by online Python code for nearly all figures, enhancing learning.
- Prepares students for advanced topics in machine learning with a sequel.
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