Genetic Algorithms and Machine Learning for Programmers: Create AI Models and Evolve Solutions (Pragmatic Programmers)
Authors: Frances Buontempo
ISBN-10: 168050620X
ISBN-13: 9781680506204
Edition 版本: 1
Released: 2019-02-02
Print Length 页数: 236 pages
Book Description
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.
Contents
Preface
Who ls This Book For?
What’s in This Book?
Online Resources
PAcnowledamente
dAcKnowIeagments
1. Escape! Code Your Way Out of a Paper Bag
Let’s Begin
Your Mission: Find a Way Out
How to Help the Turtle Escape
Let’s Save the Turtle
Did It Work?
Over to You
2. Decide! Find the Paper Bag
Your Mission: Learn from Data
PHow to Grow a pecisian Tree
Let’s Find That Paper Bag
Did It Work?
Over to You
3. Boom! Create a Genetic Algorithm
Your Mission: Fire Cannonballs
How to Breed Solutions
Let’s Fire Some Cannons
Did It Work?
Over to You
4. Swarm! Build a Nature-Inspired Swarm
Your Mission: Crowd Control
How to Form a Swarm
ILet’s Make a Swarm
Did It Work?
Over to You
5. Colonize! Discover Pathways
Your Mission: Lay Pheromones
How to Create Pathways
Let’s March Some Ants
PED-D-
fDid It Work?
Over to You
6. Diffuse! Employ a Stochastic Model
Your Mission: Make Small Random Steps
How to Cause Diffusion
Let’s Diffuse Some Particles
Did It Work?
Over to You
7. Buzz! Converge on One Solution
Your Mision: Beekeeping
our MiSsionG beekeeping
How to Feed the Bees
ILets Make Some Bees Swarm
Did It Work?
Over to You
8. Alive! Create Artificial Life
Your Mission: Make Cells Come Alive
How to Create Artificial Life
Let’s Make Cellular Automata
Did It Work?
Over to You
XIWW.
9. Dream! Explore CA with GA
Your Mission: Find the Best
How to Explore a CA
Lets Find the Best Starting Row
Did It Work?
Over to You
10. Optimize! Find the Best
Your Mission: Move Turtles
How to Get a Turtle into a Paper Bag
Let’s Find the Bottom of the Bag
Did It Work?
Extension to More Dimensions
Over to You
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