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90480384
Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning)
85% of respondents would recommend this to a friend
Item #:
90480384
XAF 114691
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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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What Stands Out
Advanced Techniques
Covers cutting-edge probabilistic methods, empowering readers to tackle complex machine learning challenges with the latest advancements in the field.
Comprehensive Scope
Addresses a wide array of topics, making it suitable for both practitioners and researchers looking to deepen their understanding of probabilistic approaches.
Expert Insights
Written by leading experts, offering invaluable perspectives and applications that enhance practical knowledge in machine learning and its adaptive computation.
Product Details
- 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 distributionsExplores how to use probabilistic models and inference for causal inference and decision makingFeatures online Python code accompaniment
| Publisher | MIT Press |
| Publication date | 15 Aug. 2023 |
| Language | English |
| Print length | 1360 pages |
| ISBN-10 | 0262048434 |
| ISBN-13 | 978-0262048439 |
| Item weight | 2.26 kg |
| Dimensions | 21.3 x 5.5 x 23.6 cm |
Who Should Buy?
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Advanced Students
Ideal for graduate students looking for in-depth knowledge in probabilistic models and advanced machine learning techniques.
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Research Professionals
Suitable for researchers in machine learning who need comprehensive reference material on state-of-the-art probabilistic methods.
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Implementers
Helpful for practitioners implementing complex machine learning solutions requiring advanced probabilistic approaches and models.
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Beginners
Not suitable for newcomers to machine learning needing foundational concepts or an overview of basic techniques.
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AI & Machine Learning Editorial Review
Probabilistic Machine Learning Advanced Topics Adaptive Computation And Machine Learning is a comprehensive text published by MIT Press on August 15, 2023. This 1360-page book offers in-depth insights into advanced topics in probabilistic machine learning, making it a valuable resource for both researchers and practitioners in the field. Readers have appreciated its well-structured content and clarity, making complex topics more accessible. The substantial length allows for thorough exploration and discussion, enhancing understanding for those who seek advanced knowledge. Whether you're in academia or industry, this text can significantly bolster your grasp of machine learning principles and practices.
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Pros
- Thorough exploration of advanced machine learning topics
- Well-structured content enhances comprehension
- Suitable for both researchers and practitioners
- Valuable resource for academic and industry professionals
- Clear discussions make complex ideas accessible
Cons
- Heavy and might be cumbersome to handle
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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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