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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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A must-see for machine learning engineers!
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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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Važne informacije
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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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