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Math for Programming
Brief Description
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 optimising search algorithms, building physics engines for games, or training neural networks, success depends on your grasp of core mathematical...
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Math for Programming
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See more"Covers essential mathematical topics for software engineers, including number representation systems, set theory, Boolean algebra, discrete mathematics, probability, statistics, linear algebra, and calculus. Focuses on practical applications and numerical methods relevant to software development, with hands-on examples in Python and C"--
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Description
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 optimising 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 optimisation 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 optimise algorithms and drive simulations
- Use probability and statistics to model uncertainty and analyse 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.
Book Details
INFORMATION
ISBN: 9781718503588
Publisher: No Starch Press,US
Format: Paperback / softback
Date Published: 22 April 2025
Country: United States
Imprint: No Starch Press,US
Audience: Professional and scholarly
DIMENSIONS
Width: 177.0mm
Height: 234.0mm
Weight: 0g
Pages: 504
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).
Also by Ronald T. Kneusel
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