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About the Author
Frances Buontempo is the editor of ACCU's Overload magazine (https://accu.org/index.php/journal/overload_by_cover). She has published articles and given talks centered on technology and machine learning. With a PhD in data mining, she has been programming professionally since the 1990s. During her career as a programmer, she has championed unit testing, mentored newer developers, deleted quite a bit of code and fixed a variety of bugs.
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Product details
Series: Pragmatic Programmers
Paperback: 236 pages
Publisher: Pragmatic Bookshelf; 1 edition (February 2, 2019)
Language: English
ISBN-10: 168050620X
ISBN-13: 978-1680506204
Product Dimensions:
7.5 x 0.5 x 9.2 inches
Shipping Weight: 14.7 ounces (View shipping rates and policies)
Average Customer Review:
5.0 out of 5 stars
1 customer review
Amazon Best Sellers Rank:
#452,603 in Books (See Top 100 in Books)
I am a software developer by trade and I have always been interested specifically in artificial intelligence (and to a lesser degree, computer learning). This book is an overview of some of the subtopics in these fields, though it is not typical in the approach. Instead of addressing each in a theoretical, scholarly manner like a textbook, it instead provides a much more hands-on approach and leads the reader through a series of exercises designed to demonstrate the material. The approach is engaging and actually fun.First, a word about computer languages: the author explains that the primary languages in used here are Python, C++ and Javascript, though other languages can be used. I personally like Perl and C++, so as I work through the examples and learn I have been using Perl, and it works well enough for me. I could possibly make life easier on myself by using a different language, but the idea is to learn the material, not struggle with a computer language.Also: It really helps to have a mathematical background. The author presents several mathematical formulas to back up the material and explain the methods, and for someone who isn’t mathematically inclined these will potentially be intimidating. If you aren’t strong in math this will hinder understanding but will not be a complete show-stopper.The author presents ten exercises that demonstrate an abstract topic in AI and/or machine learning. The first exercise is described as helping a turtle escape a bag. This is the same base process as a breadth-first search, though there are some additional factors in the overall system that complicate the calculations. The next exercise builds on the first and recreates the bag with data captured from the first exercise. Not all exercises are related, though, and many are independent of the others. One particular exercise provides an interesting approach to creating artificial life using the classic Game of Life as a starting point to a more complex variation.The formula this book follows is consistent across all of the exercises. The author presents the problem to be solved, a mission statement, a how-to section, implementation, analysis of the result and finally topics for further learning. This works well.This is not a comprehensive study on either machine learning or artificial intelligence. If it was so, it would be a huge book weighing several pounds (and probably would have more errors due to the sheer quantity of material). What is here, though, is a good starting point with a fun introduction. As I worked with this book I felt it would be a good book for use in a classroom as a lab book for advanced students.If you have an interest in computer learning or artificial intelligence, this is a nice starting point. It’s not as heady and intellectual as many others are and it does not rely on the theory to carry itself. Instead, it is a work book that can be done outside of a classroom and worked at your own pace. The material is interesting and the exercises more engaging. It isn’t a comprehensive study of the whole field, but more of an involved introduction. This is an easy book to recommend, based on my work with it.
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