Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!

Bestandsnummer des Verk�ufers S_423599487

Verk�ufer kontaktieren

Diesen Artikel melden

Bibliografische Details

Titel: Math for Programmers: 3D graphics, machine learning, and simulations with Python
Verlag: Manning
Ver�ffentlichungsdatum: 2021
Sprache: Englisch
ISBN-10: 1617295353
ISBN-13: 9781617295355
Bindung: Paperback
Zustand: Good

�ber diesen Titel

Inhaltsangabe

To score a job in data science, machine learning, computer graphics, and cryptography, you need to bring strong math skills to the party. Math for Programmers teaches the math you need for these hot careers, concentrating on what you need to know as a developer.

Filled with lots of helpful graphics and more than 200 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest programming fields.

Key Features

� 2D and 3D vector math

� Matrices and linear transformations

� Core concepts from linear algebra

� Calculus with one or more variables

� Algorithms for regression, classification, and clustering

� Interesting real-world examples

Written for programmers with solid algebra skills (even if they need some dusting off). No formal coursework in linear algebra or calculus is required.

About the technology

Most businesses realize they need to apply data science and effective machine learning to gain and maintain a competitive edge. To build these applications, they need developers comfortable writing code and using tools steeped in statistics, linear algebra, and calculus. Math also plays an integral role in other modern applications like game development, computer graphics and animation, image and signal processing, pricing engines, and stock market analysis.

Paul Orland is CEO of Tachyus, a Silicon Valley startup building predictive analytics software to optimize energy production in the oil and gas industry. As founding CTO, he led the engineering team to productize hybrid machine learning and physics models, distributed optimization algorithms, and custom web-based data visualizations. He has a B.S. in mathematics from Yale University and a M.S. in physics from the University of Washington.