Pre-Owned Math for Programming
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Pre-Owned Math for Programming

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Key item features

  • 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
    IndexRonald 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).US
  • Kneusel, Ronald T.
  • ISBN: 9781718503588
Current price is $49.81
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Appears new but has been read with very minimal signs of use. The cover has no visible wear. If applicable, the dust jacket is included for hard covers. Item has no missing or damaged pages, no creases or tears, and no markings such as underlining, highlighting or writing in margins. Used textbooks do not require the inclusion of supplemental materials.

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