Machine Learning with Julia: An Algorithmic Exploration
暫譯: 使用 Julia 進行機器學習：演算法探索

Deng, Jeremiah D.

Machine Learning with Julia: An Algorithmic Exploration
  • 出版商: Springer
  • 出版日期: 2026-04-17
  • 售價: $3,310
  • 貴賓價: 9.5 折 $3,144
  • 語言: 英文
  • 頁數: 422
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 9819696887
  • ISBN-13: 9789819696888
  • 相關分類: Machine Learning、DeepLearning

    • 海外代購書籍(需單獨結帳)

商品描述

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.

商品描述(中文翻譯)

本教科書以 Julia 程式語言，全面且易於理解地介紹機器學習。內容連結數學理論與實務應用，引導讀者學習基礎概念與進階演算法。各章涵蓋從 Kullback–Leibler divergence（Kullback–Leibler 散度）與 eigen-analysis（特徵值分析）等重要原理，到 deep transfer learning（深度遷移學習）與 differential privacy（差分隱私）等前沿技術，並提供清楚的說明與詳盡的演算法解析。每個主要主題皆搭配範例程式碼，讓讀者能透過實作學習，並更快速地完成實現。

本書運用 Julia 強大的機器學習生態系統，包括 Flux.jl、MLJ.jl 等函式庫，協助讀者建立穩健且符合最先進技術水準的機器學習模型。

本教科書適合學生、研究人員與專業人士閱讀，特別適合希望奠定扎實機器學習理論基礎、深入理解演算法，並從實務問題解決中獲得靈感的讀者。

作者簡介

Jeremiah D. Deng is an associate professor in School of Computing at University of Otago, New Zealand. His research interests include pattern recognition, machine learning, and stochastic optimization. He has published at top-tier venues such as PR, NN, TC, TEC, TKDE, TBE, and IJCAI, and serves on the editorial boards of Pattern Analysis and Applications (Springer) and ICT Express (Elsevier) and on the program committees of various AI conferences. Dr. Deng completed his PhD in computer science at University of Hong Kong and South China University of Technology, and has held visiting and adjunct positions at University of Adelaide and South China University of Technology. He is a Senior Member of both IEEE and ACM.

作者簡介(中文翻譯)

Jeremiah D. Deng 是紐西蘭 University of Otago School of Computing 的副教授。他的研究興趣包括 pattern recognition、machine learning 與 stochastic optimization。他曾在 PR、NN、TC、TEC、TKDE、TBE 及 IJCAI 等頂尖學術出版與會議發表論文，並擔任 Pattern Analysis and Applications（Springer）與 ICT Express（Elsevier）的編輯委員，以及多個 AI 會議的程序委員會委員。

Deng 博士於 University of Hong Kong 與 South China University of Technology 取得 computer science 博士學位，並曾於 University of Adelaide 及 South China University of Technology 擔任訪問及兼任職務。他同時是 IEEE 與 ACM 的 Senior Member。

類似商品