Название: Math for Programming: Learn the Math, Write Better Code (Final)Автор: Ronald T. Kneusel
Издательство: No Starch Press
Год: 2025
Страниц: 504
Язык: английский
Формат: epub (true)
Размер: 21.9 MB
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.
Programming is the art of transforming thought into code to accomplish a desired goal. This book seeks to improve that process by exploring the mathematics often present under the surface, if not out in the open. The topics discussed in this book are a condensed version of the mathematics required of most undergraduate computer science majors. They span foundational notions from set theory through discrete mathematics to linear algebra (essential for modern AI) to calculus. At all times, the book presents a balance between the math and the way programmers use it via examples in Python, C, and other languages where appropriate. Often, the code examples are directly relevant to everyday coding problems.
While it’s possible to be a good coder without a solid knowledge of mathematics, I argue that such knowledge will make you an even better coder. Mathematics is the second system devised by humans for encoding and manipulating patterns. Language is the first. Programming is yet another such system, arguably the third. Mathematics and programming are interdependent; skills learned in one domain transfer to the other. Logical thinking, problem-solving, and abstract reasoning are fundamental to both.
As a coder, you will eventually encounter algorithms and data structures requiring you to have a solid mathematical foundation in order to understand them well. Indeed, for many decades, Computer Science was part of the mathematics department. Theoretical Computer Science remains to this day a thoroughly mathematical enterprise.
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.
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