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<p class="x_xmsonormal"><span lang="EN-NZ" data-olk-copy-source="MessageBody">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.</span></p><p class="x_MsoNormal"><span lang="EN-NZ">By leveraging Julia’s powerful machine learning ecosystem<span data-olk-copy-source="MessageBody">—</span>including libraries such as Flux.jl, MLJ.jl, and more<span data-olk-copy-source="MessageBody">—</span>this book empowers readers to build robust, state-of-the-art machine learning models.</span></p><p class="x_MsoNormal"><span lang="EN-NZ">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.</span></p>

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Machine Learning with Julia

  • 教材

  • 发表日期：

    2026年

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  • Jeremiah D. Deng

摘要

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.