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:
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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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