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Item #:
81427698
Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)
90% of respondents would recommend this to a friend
Item #:
81427698
UZS 2444000
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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.
Product Details
| 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?
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Graduate Students
Ideal for postgraduate students specializing in machine learning or statistics seeking advanced topics and theoretical depth.
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Machine Learning Researchers
Essential for researchers looking to deepen knowledge in probabilistic models and their applications in machine learning.
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Data Scientists
Beneficial for data scientists wanting to enhance their skills in probabilistic approaches for better decision-making and predictions.
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Beginners
Not suitable for those new to machine learning, as it assumes prior knowledge and expertise in advanced concepts.
Product Description
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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Pros
- 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
Cons
- Hardcopies may contain typos not present in online versions
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