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).
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.
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
Über den Autor
Ronald T. Kneusel is a data scientist who builds deep-learning (AI) systems, as well as extensive experience with medical imaging and the development of medical devices. He earned a PhD in machine learning from the University of Colorado, Boulder, has nearly 20 years of machine learning experience in industry, and is presently pursuing deep-learning projects with L3Harris Technologies, Inc. Kneusel is also the author of Random Numbers and Computers (Springer 2018), in addition to Math for Deep Learning, Practical Deep Learning, and Strange Code—all published by No Starch Press.
Klappentext
Learn all of the core mathematical topics that professional software engineers need to know—in a single book!
This book summarizes all the core mathematical topics a typical professional software engineer needs to know. In condensing the various concepts covered in an undergraduate computer science program into a single volume, it provides an excellent starting point for independent study, or a refresher for those who haven’t been in a classroom for years. Early chapters cover preliminary subjects like number representation systems, set theory, and Boolean algebra, followed by a dive into the field of discrete mathematics, including functions, induction proofs, number theory, combinatorics, graphs, and trees. Later sections examine essential topics in probability, statistics, linear algebra, and calculus.
Rather than confine itself to abstract theory, the book focuses on practical applications and numerical methods at the level typically encountered by working software developers. In addition, hands-on code examples in Python and C make the topics concrete.
