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Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning)
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Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning)
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.
Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning)
物品 #: 90480384

Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning)

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物品 #: 90480384

HKD 1412

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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.

產品詳情

Shop Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning) online at a best price in 香港. 0262048434
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?

Suitable For
  • Advanced Students

    Ideal for graduate students looking for in-depth knowledge in probabilistic models and advanced machine learning techniques.

  • Research Professionals

    Suitable for researchers in machine learning who need comprehensive reference material on state-of-the-art probabilistic methods.

  • Implementers

    Helpful for practitioners implementing complex machine learning solutions requiring advanced probabilistic approaches and models.

Not Suitable For
  • Beginners

    Not suitable for newcomers to machine learning needing foundational concepts or an overview of basic techniques.

產品敘述

Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning)

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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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優點

  • 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

缺點

  • Heavy and might be cumbersome to handle

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HKD 1412

現在訂購，約可在下列時間收貨： 星期三, 十月 28

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特色和優勢

  • 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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