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Machine Learning with Julia

Machine Learning with Julia

An Algorithmic Exploration

Contenu

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.

Informations bibliographiques

novembre 2025, env. 418 pages, Machine Learning: Foundations, Methodologies, and Applications, Anglais
Springer EN
978-981-9696-88-8

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