Probabilistic Machine Learning : Advanced Topics
Language: English
Published by MIT Press Ltd Aug 2023, 2023
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 211.65
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Add to basketItem description from seller
Neuware - 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, Amazon, 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 distributions- Explores how to use probabilistic models and inference for causal inference and decision making- Features online Python code accompaniment.…
Seller Inventory # 9780262048439
- Title
- Probabilistic Machine Learning : Advanced Topics
- Author
- Kevin P. Murphy
- Publisher
- MIT Press Ltd Aug 2023
- Publication year
- 2023
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 0262048434
- ISBN 13
- 9780262048439
- Item weight
- 2,322 grams
- Dimensions
- 233x215x52 mm
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, Amazon, 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 distributions
- Explores how to use probabilistic models and inference for causal inference and decision making
- Features online Python code accompaniment
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About the Author
"About the title" may belong to another edition of this title.
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