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.

Explore important mathematical concepts through hands-on coding. 
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
 

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.

About the technology
Skip the mathematical jargon: This one-of-a-kind book uses Python to teach the math you need to build games, simulations, 3D graphics, and machine learning algorithms. Discover how algebra and calculus come alive when you see them in code!

About the book
In Math for Programmers you’ll explore important mathematical concepts through hands-on coding. Filled with graphics and more than 300 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, you’ll master the key Python libraries used to turn them into real-world software applications.

What's inside

    Vector geometry for computer graphics
    Matrices and linear transformations
    Core concepts from calculus
    Simulation and optimization
    Image and audio processing
    Machine learning algorithms for regression and classification

About the reader
For programmers with basic skills in algebra.

About the author
Paul Orland is a programmer, software entrepreneur, and math enthusiast. He is co-founder of Tachyus, a start-up building predictive analytics software for the energy industry. You can find him online at www.paulor.land.

Table of Contents

1 Learning math with code

PART I - VECTORS AND GRAPHICS

2 Drawing with 2D vectors

3 Ascending to the 3D world

4 Transforming vectors and graphics

5 Computing transformations with matrices

6 Generalizing to higher dimensions

7 Solving systems of linear equations

PART 2 - CALCULUS AND PHYSICAL SIMULATION

8 Understanding rates of change

9 Simulating moving objects

10 Working with symbolic expressions

11 Simulating force fields

12 Optimizing a physical system

13 Analyzing sound waves with a Fourier series

PART 3 - MACHINE LEARNING APPLICATIONS

14 Fitting functions to data

15 Classifying data with logistic regression

16 Training neural networks