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244093102
Probabilistic Machine Learning: Advanced Topics Adaptive Computation and Machine Learning series
88% of respondents would recommend this to a friend
Item #:
244093102
EGP 10312
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An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality.
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- An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, , Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.Covers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributionsExplores how to use probabilistic models and inference for causal inference and decision makingFeatures online Python code accompaniment
| Publisher | The MIT Press |
| Publication date | August 15, 2023 |
| Language | English |
| Print length | 1360 pages |
| ISBN-10 | 0262048434 |
| ISBN-13 | 978-0262048439 |
| Item Weight | 2.26 Kilograms |
| Dimensions | 8.39 x 2.17 x 9.29 inches (21.3 x 5.5 x 23.6 cm) |
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Probabilistic Machine Learning: Advanced Topics Adaptive Computation and Machine Learning series
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English Edition Kevin P. Murphy Format: Hardcover Human Vision & Language Systems Editorial Review
Probabilistic Machine Learning: Advanced Topics is a comprehensive resource published by The MIT Press, weighing 2.26 kilograms and spanning an impressive 1360 pages. Released on August 15, 2023, this book delves into the intricate aspects of machine learning with a focus on probabilistic approaches. Readers will appreciate its thorough exploration of advanced topics, making it an essential read for those looking to deepen their understanding in this field. The book's dimensions, at 8.39 x 2.17 x 9.29 inches, and its ISBN-10 of 0262048434, make it suitable for both study and reference.
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Pros
- Comprehensive coverage of advanced machine learning topics
- Published by a reputable publisher, The MIT Press
- Well-structured content for in-depth understanding
- Suitable for both students and professionals
- Recent publication ensures up-to-date information
Cons
- The book's length may be daunting for some readers
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Features & Benefits
- Researchers and graduate students in machine learning and statistics
- Deep generative modeling
- Graphical models
- Bayesian inference
- Reinforcement learning
- Causality
- Provides knowledge of crucial issues in machine learning from top scientists and domain experts
- Puts deep learning in a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference
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