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Genetic Algorithms and Machine Learning for Programmers : Create AI Models and Evolve Solutions by Frances Buontempo (2019, Trade Paperback)

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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.

About this product

Product Identifiers

Publisher
Pragmatic Programmers, LLC, T.H.E.
ISBN-10
168050620X
ISBN-13
9781680506204
eBay Product ID (ePID)
8038597478

Product Key Features

Number of Pages
250 Pages
Language
English
Publication Name
Genetic Algorithms and Machine Learning for Programmers : Create Ai Models and Evolve Solutions
Publication Year
2019
Subject
Intelligence (Ai) & Semantics, General, Computer Engineering
Type
Textbook
Subject Area
Mathematics, Computers
Author
Frances Buontempo
Format
Trade Paperback

Dimensions

Item Height
0.7 in
Item Weight
15.7 Oz
Item Length
9.3 in
Item Width
7.5 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2020-300015
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.3/823
Synopsis
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. What You Need: 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.
LC Classification Number
QA76.623.B86 2019