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.
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
About the Author
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.