- Paperback: 222 pages
- Publisher: CreateSpace Independent Publishing Platform; 1 edition (26 Nov. 2013)
- Language: English
- ISBN-10: 1493682229
- ISBN-13: 978-1493682225
- Product Dimensions: 19 x 1.3 x 23.5 cm
- Average Customer Review: 3.1 out of 5 stars See all reviews (7 customer reviews)
- Amazon Bestsellers Rank: 86,340 in Books (See Top 100 in Books)
Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms Paperback – 26 Nov 2013
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About the Author
Jeff Heaton, PhD, is a data scientist and indy publisher. Specializing in Python, R, Java and C#, he is an active technology blogger, 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).
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Top Customer Reviews
The part about equilateral encoding is especially painful and will leave even the most patient of readers annoyed. Heaton is the archetypical shitty coder that will use one-letter variables that represent important parts of a formula without explaining them. The part mentioned above also uses uninitialized variables with one-letter names, as if to further confuse readers.
There are a few paragraphs of text interspersed with the code that are, I assume, supposed to shed light on what is happening, but they rarely actually augment the meaning of the code or what is supposed to be happening.
This is not a good book for anyone but people who already know the material and simply want to refresh or confirm knowledge. I'm quite sure that the material is correct in scope, but it does not do a close to decent job in explaining stuff and uses downright bad code for most things in it.
All in all I think the book could have been served well by having Heaton define functions that were smaller in scope and had well defined purposes. They could then be used as abstractions (that have been well explained) and would lead to cleaner examples.Read more ›
This book is in sore need of some editing: there are a lot of grammar errors some of which make some of the explanations difficult to decipher. In some cases I had to resort to Google to untangle some terminology.
For some one who :
- is starting from scratch with AI;
- can already programme; and
- who has some maths (age 18)
the book is a perfect run thru of the basic principles patterns and terminology of AI. It does a wonderful job of ordering the introduction of concepts so that they build on each other, by small increments, in a logical way so that you are hardly aware of the transition from trivial to profound.
The book identifies key pragmatic abstractions and, despite, the unpolished drafting, does the priceless task of identifying the core of simplicity around which complexity can later be built.
The book is self published and the guy clearly edited himself which goes to show how important an objective view can be. He would really have benefited from some hard talking.
Despite this however, the fundamental cleverness and appropriateness of the approach provides a succinct introduction to the subject.
Most Recent Customer Reviews
Excellent introductory book, and don't forget to grab the online resources at gitPublished 12 months ago by sonofdubya