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 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. What You Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (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 algorithm descriptions.

236 pages, Paperback

Published February 26, 2019

Reviews

[Eric · Rating 2 out of 5]

This book may have an audience; I am just not part of it.

I bought it to refresh old knowledge on GA, expecting to review under the perspective of a programmer—I was in an academic environment last time I used most techniques in the book.

Unfortunately I find the book too shallow on the algorithmic and programming aspects. The method is clear, but code requires knowing 3 different languages (Python, JavaScript, and C++), which distracts from the core—a single, concise language like Python would have simplified as well as deepened the exposé. Also, the recurring problem approached with all algorithms is simple enough to illustrate, but really not engaging (personal rant: a typical problem for boring interviews). On top of that, many digressions along the chapters are both appealing and a bit too frequent, which is frustrating.

In short, I may have just not enjoyed the style of the book. Yet, beyond style, I cannot recommend it to either programmers or people who just want a refresher/overview.

[Steve Love · Rating 5 out of 5]

This book is great if you're a programmer with little or no technical expertise in machine learning or genetic algorithms, and wants a leg-up with some accessible and repeatable examples to try. The code samples demonstrate some of the key principles used in "real" machine learning systems, but demonstrate them through some fun visualizations and little problems that help to focus the attention. It's not a big heavy comprehensive reference, but a convenient-to-carry size with some choice topics.

Several different programming languages are used throughout the book, but the problems are small and simple enough that a reasonably competent programmer shouldn't have much difficulty in translating specific examples to the language of their choice.

All in all, you may not get much from this book if you're already an expert, or have a technical background in machine learning/genetic algorithms already, but if you're trying to obtain that understanding from a standing start, or even just have a curiosity of the subject, you'll definitely find this book useful.

[Steve Brock · Rating 5 out of 5]

This book was a Best of the Best for the month of June, 2019, as selected by Stevo's Book Reviews on the Internet. You can find me at http://forums.delphiforums.com/stevo1, on my Stevo's Novel Ideas Amazon Influencer page (https://www.amazon.com/shop/stevo4747) or search for me on Google for many more reviews and recommendations.

[Irwind · Rating 2 out of 5]

Not a great book. Has some good explanations at times, but it switch's from python to JavaScript to c++. I would prefer it to stay in one language. Also the references are to random webpages and Wikipedia articles, which is a big red flag.