Math for Programming
tra 2025. · No Starch Press
E-knjiga
504
str.
family_home
Ispunjava uvjete
info
reportOcjene i recenzije nisu potvrđene Saznajte više
O ovoj e-knjizi
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:
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.
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.
O autoru
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).
Ocijenite ovu e-knjigu
Recite nam što mislite.
Informacije o čitanju
Pametni telefoni i tableti
Instalirajte aplikaciju Google Play knjige za Android i iPad/iPhone. Automatski se sinkronizira s vašim računom i omogućuje vam da čitate online ili offline gdje god bili.
Prijenosna i stolna računala
Audioknjige kupljene na Google Playu možete slušati pomoću web-preglednika na računalu.
Elektronički čitači i ostali uređaji
Za čitanje na uređajima s elektroničkom tintom, kao što su Kobo e-čitači, trebate preuzeti datoteku i prenijeti je na svoj uređaj. Slijedite detaljne upute u centru za pomoć za prijenos datoteka na podržane e-čitače.
