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- Pythonで動かして学ぶ! あたらしいベイズ統計の教科書 AI & TECHNOLOGY...
Stavka #:
170323720
Pythonで動かして学ぶ! あたらしいベイズ統計の教科書 AI & TECHNOLOGY
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Stavka #:
170323720
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What Stands Out
Hands-On Learning
Engages readers with practical Python examples, reinforcing Bayesian statistics concepts through real-world applications and interactive elements.
Modern Approach
Covers contemporary Bayesian techniques and tools, making it suitable for both beginners and those familiar with statistics who seek to update their knowledge.
Comprehensive Resource
Serves as a thorough guide to Bayesian statistics, integrating theory with coding practices to enhance understanding and applicability in various fields.
Detalji o proizvodu
| Publisher | u7fd4u6cf3u793e |
| Publication date | August 5, 2021 |
| Language | Japanese |
| Print length | 256 pages |
| ISBN-10 | 4798168645 |
| ISBN-13 | 978-4798168647 |
| Item Weight | 340 g |
| Dimensions | 5.91 x 0.63 x 8.35 inches (15 x 1.6 x 21.2 cm) |
| Part of series | AI & TECHNOLOGY |
Who Should Buy?
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Statistics Students
Ideal for students seeking a practical approach to Bayesian statistics using Python for hands-on learning.
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Data Scientists
Beneficial for data scientists who want to deepen their understanding of Bayesian methods through practical implementation.
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Educators
Great resource for educators looking to incorporate real-world applications of Bayesian statistics into their curriculum.
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Beginners in Statistics
Not suitable for those without a foundational knowledge of statistics, as it may overwhelm them.
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Pythonで動かして学ぶ! あたらしいベイズ統計の教科書 AI & TECHNOLOGY
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かくあき Format: Tankobon Softcover Probability & Statistics Editorial Review
Pythonで動かして学ぶ! あたらしいベイズ統計の教科書 offers a practical approach to Bayesian statistics with Python programming. Published by 笠井社 in August 2021, this 256-page book is designed in Japanese, making it accessible to a wide audience interested in AI and technology. It also serves as part of the AI & TECHNOLOGY series, providing an excellent foundation for learners looking to deepen their understanding of statistical methods. Readers appreciate its clarity and hands-on examples that bolster comprehension of complex concepts.
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Pros
- Clear explanations of Bayesian statistics
- Hands-on examples enhance learning
- Suitable for beginners in statistics
- Part of a popular AI & TECHNOLOGY series
- Well-structured with practical applications
Protiv
- May require basic Python knowledge
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Značajke i prednosti
- Comprehensive guide to Bayesian statistics tailored for machine learning engineers.
- Explains Bayesian concepts from fundamentals to modeling.
- Hands-on examples using Python, specifically with the PyMC3 library.
- Covers crucial topics such as Bayes' theorem and MCMC methods.
- Ideal for beginners and those seeking to enhance their statistical modeling skills.
- Structured for ease of understanding and practical application.
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