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Math for Programming
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Math for Programming Paperback - 2025

by Ronald T. Kneusel

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From the publisher

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.

Details

  • Title Math for Programming
  • Author Ronald T. Kneusel
  • Binding Paperback
  • Pages 504
  • Volumes 1
  • Language ENG
  • Publisher No Starch Press
  • Publication date 2025-04-22
  • Illustrated Yes
  • Features Illustrated, Index
  • ISBN 9781718503588 / 171850358X
  • Weight 2.1 lbs (0.95 kg)
  • Dimensions 9.2 x 7 x 1.2 in (23.37 x 17.78 x 3.05 cm)
  • Category Computers - General Information
  • Library of Congress subjects Mathematics, Computer programming - Mathematics
  • Library of Congress Catalogue Number 2024044258
  • Dewey Decimal Code 510.240

About the author

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).

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