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Machine Learning with Julia: An Algorithmic Exploration (Machine Learning: Foundations, Methodologies, and Applications)
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- 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.
| Publisher | Springer |
| Publication date | April 17, 2026 |
| Language | English |
| Print length | 445 pages |
| ISBN-10 | 9819696887 |
| ISBN-13 | 978-9819696888 |
| Item Weight | 2.73 pounds (1.24 kg) |
| Dimensions | 8.59 x 1.14 x 11.06 inches (21.8 x 2.9 x 28.1 cm) |
Product Description
Machine Learning with Julia: An Algorithmic Exploration (Machine Learning: Foundations, Methodologies, and Applications)
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