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Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)
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Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)
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 series)
商品＃: 81427698

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

88% of respondents would recommend this to a friend

商品＃: 81427698

TWD 6452

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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

In-Depth Insights
Offers advanced analysis of probabilistic methods, enabling readers to grasp complex concepts efficiently and apply them in real-world scenarios.
Comprehensive Coverage
Covers a broad range of topics, from foundational principles to cutting-edge techniques, catering to both researchers and practitioners in machine learning.
Practical Applications
Integrates theoretical knowledge with practical applications, ensuring that readers can translate learning into actionable solutions in various fields.

產品詳情

Shop Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series) online at a best price in Taiwan. 0262048434
Publisher The MIT Press
Publication date August 15, 2023
Language English
Print length 1360 pages
ISBN-10 0262048434
ISBN-13 978-0262048439
Item Weight 4.98 pounds (2.26 kg)
Dimensions 8.39 x 2.17 x 9.29 inches (21.3 x 5.5 x 23.6 cm)

Who Should Buy?

Suitable For
  • Graduate Students

    Ideal for postgraduate students specializing in machine learning or statistics seeking advanced topics and theoretical depth.

  • Machine Learning Researchers

    Essential for researchers looking to deepen knowledge in probabilistic models and their applications in machine learning.

  • Data Scientists

    Beneficial for data scientists wanting to enhance their skills in probabilistic approaches for better decision-making and predictions.

Not Suitable For
  • Beginners

    Not suitable for those new to machine learning, as it assumes prior knowledge and expertise in advanced concepts.

產品描述

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

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Intelligence & Semantics Editorial Review

Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series) is an extensive textbook published by The MIT Press on August 15, 2023, that spans 1360 pages and covers a breadth of advanced topics in machine learning. Readers have praised its depth and detail, especially regarding critical concepts like matrix calculus and gradient descent, making it suitable for those looking to deepen their understanding of ML. The engaging style of the author, Kevin Murphy, who is recognized for his ability to blend teaching with research, contributes to the book’s appeal. While the book is primarily aimed at graduate students, even those with foundational knowledge in regression can benefit, despite the learning curve involved. The accompanying online resources enhance the learning experience, although some readers mentioned minor issues like errors in hardcopy versions versus updates on the author's website.

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優點

  • Comprehensive coverage of advanced machine learning topics
  • Engaging teaching style from a renowned author
  • Valuable online resources complement the textbook
  • Impressive graphics enhance understanding
  • Suitable for both beginners and experienced learners

缺點

  • Hardcopies may contain typos not present in online versions

Product Price History

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TWD 6452

立即訂購並活動它 Monday, 十月 05

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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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