Artificial Intelligence for Humans, Volume 2: Nature-Inspired Algorithms

Heaton, Jeff

ISBN 10: 1499720572 / ISBN 13: 9781499720570

Published by CreateSpace Independent Publishing Platform, 2014

About this Item

Seller Inventory # 1499720572-4-36426332

Bibliographic Details

• Title: Artificial Intelligence for Humans, Volume 2: Nature-Inspired Algorithms
• Publisher: CreateSpace Independent Publishing Platform
• Publication Date: 2014
• Language: English
• ISBN 10: 1499720572
• ISBN 13: 9781499720570
• Binding: paperback
• Condition: Fair
• Weight: 17 ounces

About this title

Synopsis

Nature can be a great source of inspiration for artificial intelligence algorithms because its technology is considerably more advanced than our own. Among its wonders are strong AI, nanotechnology, and advanced robotics. Nature can therefore serve as a guide for real-life problem solving. In this book, you will encounter algorithms influenced by ants, bees, genomes, birds, and cells that provide practical methods for many types of AI situations. Although nature is the muse behind the methods, we are not duplicating its exact processes. The complex behaviors in nature merely provide inspiration in our quest to gain new insights about data. Artificial Intelligence for Humans is a book series meant to teach AI to those readers who lack an extensive mathematical background. The reader only needs knowledge of basic college algebra and computer programming. Additional topics are thoroughly explained. Every chapter also includes a programming example. Examples are currently provided in Java, C#, and Python. Other languages are planned. No knowledge of biology is needed to read this book. With a forward by Dave Snell.

About the Author

Jeff Heaton, PhD, is a computer scientist that specializes in data science and artificial intelligence. Specializing in Python, R, Java and C#, he is an open source contributor and author of more than ten books. His areas of expertise include predictive modeling, data mining, big data, business intelligence, and artificial intelligence. Jeff holds a Master's Degree in Information Management from Washington University and a PhD in computer science from Nova Southeastern University in computer science. He is the lead developer for the Encog Machine Learning Framework open source project, a senior member of IEEE, and a fellow of the Life Management Institute (FLMI).