Math for Programming
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Book 1
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Book 1
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Math for Programming

Math for Programming


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About the Book

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

Table of Contents:
Foreword Acknowledgments Introduction Chapter 1. Computers and Numbers Chapter 2. Sets and Abstract Algebra Chapter 3. Boolean Algebra Chapter 4. Functions and Relations Chapter 5. Induction Chapter 6. Recurrence and Recursion Chapter 7. Number Theory Chapter 8. Counting and Combinatorics Chapter 9. Graphs Chapter 10. Trees Chapter 11. Probability Chapter 12. Statistics Chapter 13. Linear Algebra Chapter 14. Differential Calculus Chapter 15. Integral Calculus Chapter 16. Differential Equations Index

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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Product Details
  • ISBN-13: 9781718503588
  • Publisher: No Starch Press,US
  • Publisher Imprint: No Starch Press,US
  • Height: 234 mm
  • No of Pages: 504
  • Returnable: Y
  • ISBN-10: 171850358X
  • Publisher Date: 22 Apr 2025
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Width: 177 mm


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