Math for Programming
暫譯: 程式設計的數學

Kneusel, Ronald T.

Math for Programming
  • 出版商: No Starch Press
  • 出版日期: 2025-04-22
  • 售價: $1,890
  • 貴賓價: 9.5 折 $1,796
  • 語言: 英文
  • 頁數: 504
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 171850358X
  • ISBN-13: 9781718503588
  • 相關分類: R 語言

    • 海外代購書籍(需單獨結帳)

商品描述

A one-stop-shop for all the math you should have learned for your programming career.

Every great programming challenge has mathematical principles at its heart. Whether you're optimizing search algorithms, building physics engines for games, or training neural networks, success depends on your grasp of core mathematical concepts.

In Math for Programming, you'll master the essential mathematics that will take you from basic coding to serious software development. You'll discover how vectors and matrices give you the power to handle complex data, how calculus drives optimization and machine learning, and how graph theory leads to advanced search algorithms.

Through clear explanations and practical examples, you'll learn to:

  • Harness linear algebra to manipulate data with unprecedented efficiency
  • Apply calculus concepts to optimize algorithms and drive simulations
  • Use probability and statistics to model uncertainty and analyze data
  • Master the discrete mathematics that powers modern data structures
  • Solve dynamic problems through differential equations

Whether you're seeking to fill gaps in your mathematical foundation or looking to refresh your understanding of core concepts, Math for Programming will turn complex math into a practical tool you'll use every day.

商品描述(中文翻譯)

一站式學習所有你應該為程式設計職業所學的數學。

每一個偉大的程式設計挑戰背後都有數學原則。無論你是在優化搜尋演算法、為遊戲構建物理引擎，還是訓練神經網絡，成功都取決於你對核心數學概念的掌握。

在程式設計數學中，你將掌握從基本編碼到嚴謹軟體開發所需的基本數學知識。你將發現向量和矩陣如何使你能夠處理複雜數據，微積分如何推動優化和機器學習，以及圖論如何導致先進的搜尋演算法。

通過清晰的解釋和實用的範例，你將學會：

  • 利用線性代數以空前的效率操作數據

  • 應用微積分概念來優化演算法和驅動模擬

  • 使用機率和統計來建模不確定性和分析數據

  • 掌握驅動現代數據結構的離散數學

  • 通過微分方程解決動態問題


無論你是想填補數學基礎的空白，還是希望刷新對核心概念的理解，程式設計數學將使複雜的數學變成你每天都會使用的實用工具。

作者簡介

Ronald T. Kneusel has been working with machine learning in industry since 2003 and has a PhD in machine learning from the University of Colorado, Boulder. Kneusel is the author of Practical Deep Learning, Math for Deep Learning, The Art of Randomness, How AI Works, and Strange Code (all from No Starch Press), as well as Numbers and Computers and Random Numbers and Computers (Springer).

作者簡介(中文翻譯)

Ronald T. Kneusel 自 2003 年以來一直在業界從事機器學習，並擁有科羅拉多大學博爾德分校的機器學習博士學位。Kneusel 是 Practical Deep Learning、Math for Deep Learning、The Art of Randomness、How AI Works 和 Strange Code（均由 No Starch Press 出版）的作者，以及 Numbers and Computers 和 Random Numbers and Computers（Springer 出版）。

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