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A great building requires a strong foundation. This book teaches basic Artificial Intelligence algorithms such as dimensionality, distance metrics, clustering, error calculation, hill climbing, Nelder Mead, and linear regression. These are not just foundational algorithms for the rest of the series, but are very useful in their own right. The book explains all algorithms using actual numeric calculations that you can perform yourself. Artificial Intelligence for Humans is a book series meant to teach AI to those without an extensive mathematical background. The reader needs only a knowledge of basic college algebra or computer programming—anything more complicated than that is thoroughly explained. Every chapter also includes a programming example. Examples are currently provided in Java, C#, R, Python and C. Other languages planned.
- Sales Rank: #130404 in Books
- Published on: 2013-11-26
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .50" w x 7.50" l, .86 pounds
- Binding: Paperback
- 222 pages
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).
Most helpful customer reviews
19 of 21 people found the following review helpful.
A Basic Introduction to Machine Learning
By David Taylor
This book claims to be an overview of artificial intelligence, but it’s not; it’s an overview of machine learning. It’s true that machine learning is a hot topic within AI just now, but it's hardly taken over the field, nor has it rendered all other methods obsolete. But, if you just want an informal introduction to the basic forms of machine learning, it's short and easy to read. The rubber never quite meets the road, but if all you need is the basic concepts, it's not a bad start.
It does, however, contain errors that really should have been caught prior to publication. In addition to the errors mentioned by another reviewer, the references to equations 10.2 through 10.4 are wrong, and the description of the logistic function shown in Figure 10.3 doesn’t match the function shown. The notation specifies the curve as going from 0 to , but it is drawn from 1 to 0, which is backward from what the author intended. Also, the curve is described in the text as a logit function, which the author seems to confuse with the logistic. They both has S shapes, but they are very different things with different roles to play in terms of how they bound their values. To put it graphically, the S of a logit is horizontal, with the tails extending up and down, not vertical with tails to the left and right as shown in the figure.
22 of 24 people found the following review helpful.
A good primer for AI
By Brookemeister
I've wanted to better understand artificial intelligence for a long time. This book has opened the door for me. It requires a little mathematical aptitude, but little else, as the author starts with basic concepts and gradually builds on them. I like the examples and illustrations. They helped me digest and build my understanding one step at a time.
I've been in contact with the author and found out that, with self-publishing, you can pretty immediately turn around reader feedback and make incremental improvements to the book. Since I bought the book last month, a number of improvements have already been made.
2 of 2 people found the following review helpful.
Mainly numerical optimization methods
By Kindle Customer
Although it does talk the traveling salesman problem and knapsack problems mostly it talks about curve fitting and maximization, while these are important to know about in some circumstances, I'm not sure I call them AI.
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