Practical Agile AI with Pharo: Artificial Intelligence Techniques Through Algorithm Programming

Name: Practical Agile AI with Pharo: Artificial Intelligence Techniques Through Algorithm Programming
Brand: Apress
SKU: 9781484253830
Price: 4568.0 INR
Availability: InStock

by Alexandre Bergel

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ISBN13: 9781484253830
Binding: Paperback
Subject: N/A
Publisher: Apress
Publisher Imprint: Apress
Publication Date: June 21, 2020
Pages: 265
Original Price: USD 64.99
Language: English
Edition: N/A
Item Weight: 708 grams
BISAC Subject(s): Languages / General, Artificial Intelligence / General, and Programming / Object Oriented

About The Book

From the Back Cover
Cover classical algorithms commonly used as artificial intelligence techniques and program agile artificial intelligence applications using Pharo. This book takes a practical approach by presenting the implementation details to illustrate the numerous concepts it explains.
Along the way, you'll learn neural net fundamentals to set you up for practical examples such as the traveling salesman problem and cover genetic algorithms including a fun zoomorphic creature example. Furthermore, Practical Agile AI with Pharo finishes with a data classification application and two game applications including a Pong-like game and a Flappy Bird-like game. This book is informative and fun, giving you source code to play along with. You'll be able to take this source code and apply it to your own projects.

You will:

Use neurons, neural networks, learning theory, and more
Work with genetic algorithms
Incorporate neural network principles when working towards neuroevolution
Include neural network fundamentals when building three Pharo-based applications

About The Author

Alexandre Bergel, Ph.D., is an associate professor in the Department of Computer Science (DCC) at the University of Chile and is a member of the Intelligent Software Construction laboratory (ISCLab). His research interests include software engineering, software performance, software visualization, programming environment, and machine learning. He is interested in improving the way we build and maintain software. His current hypotheses are validated using rigorous empirical methodologies. To make his research artifacts useful not only to stack papers, he co-founded Object Profile.

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