The Art of Randomness cover

The Art of Randomness

Using Randomized Algorithms in the Real World
by Ronald T. Kneusel
January 2024, 400 pp.
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Download Chapter 6: Computer Science Algorithms

Look Inside!

Art of Randomness pages 22-23Art of Randomness pages 34-35Art of Randomness pages 42-43Art of Randomness pages 90-91Art of Randomness pages 142-143Art of Randomness pages 196-197Art of Randomness pages 232-233Art of Randomness pages 244-245

When properly applied, randomness can be a powerful tool in programming, science, and art. This highly practical but geekily fun introduction to randomness shows you how to put chaos to work, illustrating its ability to power everything from the simulation of Darwinian evolution, to product placement in a grocery store, to hiding information in plain sight, and even how to generate art and music.

By encouraging you to engage in "what if" speculation, you’ll build intuition about when and how to use randomness to get things done. Each chapter describes how randomness plays into the given topic area, then proceeds to demonstrate its problem-solving role with hands-on experiments to work through using Python code. By the end of the book, you’ll see why randomness belongs in every programmer’s toolbox.

You'll learn how to:

  • Explore the mathematical background of randomness
  • Use randomness for encrypting messages, creating models, and implementing swarm-intelligence or machine-learning algorithms
  • Discover how randomness is used in programming applications, and apply it  to your own work
Author Bio 

Ronald T. Kneusel is a data scientist who builds deep-learning (AI) systems, as well as extensive experience with medical imaging and the development of medical devices. He earned a PhD in machine learning from the University of Colorado, Boulder, has nearly 20 years of machine learning experience in industry, and is presently pursuing deep-learning projects with L3Harris Technologies, Inc. Kneusel is also the author of Random Numbers and Computers (Springer 2018), in addition to Math for Deep Learning, Practical Deep Learning, and Strange Code—all published by No Starch Press.

Table of contents 

Chapter 1: Generating Randomness
Chapter 2: Steganography
Chapter 3: Modeling the World
Chapter 4: Swarm Intelligence and Evolutionary Algorithms
Chapter 5: More Swarm Intelligence and Evolutionary Algorithms
Chapter 6: Random Intelligence
Chapter 7: Generative Art
Chapter 8: Generative Music
Chapter 9: Compressed Sensing
Chapter 10: Experimental Design
Chapter 11: Randomized Algorithms
Chapter 12: Sampling
Appendix A: Resources


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The chapters in red are included in this Early Access PDF.