Pages: 234 Published: January 2019 ISBN: 9781680506204 Edition: 1
Genetic Algorithms and Machine Learning for Programmers
Create AI Models and Evolve Solutions
by Frances Buontempo
Self-driving cars, natural language recognition, and online
recommendation engines are all possible thanks to Machine Learning. Now
you can create your own genetic algorithms, nature-inspired swarms,
Monte Carlo simulations, cellular automata, and clusters. Learn how to
test your ML code and dive into even more advanced topics. If you are a
beginner-to-intermediate programmer keen to understand machine learning,
this book is for you.
Discover machine learning algorithms using a handful of self-contained
recipes. Build a repertoire of algorithms, discovering terms and
approaches that apply generally. Bake intelligence into your algorithms,
guiding them to discover good solutions to problems.
In this book, you will:
Use heuristics and design fitness functions.
Build genetic algorithms.
Make nature-inspired swarms with ants, bees and particles.
Create Monte Carlo simulations.
Investigate cellular automata.
Find minima and maxima, using hill climbing and simulated annealing.
Try selection methods, including tournament and roulette wheels.
Learn about heuristics, fitness functions, metrics, and clusters.
Test your code and get inspired to try new problems. Work through
scenarios to code your way out of a paper bag; an important skill for
any competent programmer. See how the algorithms explore and learn by
creating visualizations of each problem. Get inspired to design your own
machine learning projects and become familiar with the jargon.
(using the HTML5 canvas). Also uses matplotlib and some open source
libraries, including SFML, Catch and Cosmic-Ray. These plotting and
testing libraries are not required but their use will give you a fuller
experience. Armed with just a text editor and compiler/interpreter for
your language of choice you can still code along from the general
Frances Buontempo is the editor of ACCU’s Overload magazine
(https://accu.org/index.php/journal/overload_by_cover). She has
published articles and given talks centered on technology and machine
learning. With a PhD in data mining, she has been programming
professionally since the 1990s. During her career as a programmer, she
has championed unit testing, mentored newer developers, deleted quite a
bit of code and fixed a variety of bugs.