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Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms Paperback – November 26, 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
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The chapter are very short and 1/3 to 1/2 of the text introduce and summarize the chapter leaving only couple pages for the content itself.
The content itself is barely scratching the surface and is poorly written.
There are many great introductory books and online article on the topic. Save your money
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.
Re "Data Science", I have been designing a system to feed ginat quantities of data to data scientists, so I thought I should know better what, in fact, "data science" was, so I bought some books. Most of the machine learning and modeling are actually the same, but this book explained in a few hundred pages what the data science books struggled to, in aggregate, in over a thousand pages! (I was blaming myself, until I read this book, in fact, since when ten external sources say you're wrong, and you say you're right, you're probably wrong :))