Machine Learning with Julia An Algorithmic Exploration
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Form:Einzelkauf Download
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Sprache:Englisch
Fr. 88.90
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Produktdetails
Format
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Nein
Erscheinungsdatum
16.04.2026
Verlag
Springer Nature SingaporeSeitenzahl
422 (Printausgabe)
Dateigröße
13820 KB
Sprache
Englisch
EAN
9789819696895
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.
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