Math for programmers : 3D graphics, machine learning and simulations with Python / Paul Orland.

Format:

Book

Author/Creator:

Orland, Paul, author.

Language:

English

Subjects (All):

Mathematics--Study and teaching.

Mathematics.

Computer science--Study and teaching.

Computer science.

Computer programming--Study and teaching.

Computer programming.

Python (Computer program language).

Physical Description:

1 online resource (500 pages)

Place of Publication:

Shelter Island, New York : Manning, [2020]

Summary:

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! In Math for Programmers youll explore important mathematical concepts through hands-on coding. Filled with graphics and more than 200 exercises and mini-projects, this book unlocks the door to interesting and lucrative! careers in some of todays hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, youll master the key Python libraries used to turn them into real-world software applications. -- Provided by publisher.

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 todays hottest programming fields.

Contents:

Learning math with code

Part 1 Vectors and graphics

Drawing with 2D vectors

Ascending to the 3D world

Transforming vectors and graphics

Computing transformations with matrices

Generalizing to higher dimensions

Solving systems of linear equations

Part 2 Calculus and physical simulation

Understanding rates of change

Simulating moving objects

Working with symbolic expressions

Simulating force fields

Optimizing a physical system

Analyzing sound waves with a Fourier series

Part 3 Machine learning applications

Fitting functions to data

Classifying data with logistic regression

Training neural networks

Appendix A Getting set up with Python

Appendix B Python tips and tricks

Appendix C Loading and rendering 3D Models with OpenGL and PyGame

Notes:

Description based on print version record.

Includes index.

ISBN:

9781638357070

1638357072

OCLC:

1257078217