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商品詳細
Machine Learning with Julia: An Algorithmic Exploration.
・ISBN 978-981-9696-88-8 hard EUR 69.99
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| 著者・編者 | Deng, Jeremiah D., |
|---|---|
| シリーズ | (Machine Learning: Foundations, Methodologies, and Applications) |
| 出版社 | (Springer Nature Switzerland AG, SZ) |
| 出版年月 | 2026 |
| ページ数 | 422 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A04-57942＞ |
解説
This textbook offers a comprehensive and accessible introduction to machine learning with the Julia programming language. It bridges mathematical theory and real-world practice, guiding readers through both foundational concepts and advanced algorithms. Covering topics from essential principles like Kullback-Leibler divergence and eigen-analysis to cutting-edge techniques such as deep transfer learning and differential privacy, each chapter delivers clear explanations and detailed algorithmic treatments. Sample code accompanies every major topic, enabling hands-on learning and faster implementation.
By leveraging Julia's powerful machine learning ecosystem-including libraries such as Flux.jl, MLJ.jl, and more-this book empowers readers to build robust, state-of-the-art machine learning models.
Ideal for students, researchers, and professionals alike, this textbook is designed for those seeking a solid theoretical foundation in machine learning, along with deep algorithmic insight and practical problem-solving inspiration.