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Programming Collective Intelligence: Building Smart Web 2.0 Applications Paperback – August 23, 2007
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About the Author
More About the Author
He currently holds the title of Data Magnate at Metaweb Technologies, where he works on large-scale data reconciliation problems. He is also a cofounder of freerisk.org, a non-profit aimed at creating more financial transparency.
Prior to Metaweb he founded Incellico, a biotechbology software company acquired in 2003. He holds a B.Sc. in Computer Science from MIT and US Government deems him a "Person of Exceptional Ability."
Top Customer Reviews
My area of strength happens to be neural networks (my MS thesis topic was in the subject), so I will focus on that. In a few pages of the book, the author describes how the most popular of all neural networks, backpropagation, can be used to map a set of search terms to a URL. One might do this, for example, to try and find the page best matching the search terms. Instead of doing what nearly all other authors will do, prove the math behind the backprop training algorithm, he instead mentions what it does, and goes on to present python code that implements the stated goal.
The upside of the approach is clear -- if you know the theory of neural networks, and are not sure how to apply it (or want to see an example of how it can be applied), then this book is great for that. His example of adaptively training a backprop net using only a subset of the nodes in the network was interesting, and I learned from it. Given all the reading I have done over the years on the subject, that was a bit of a surprise for me.
However, don't take this book as being the "end all, be all" for understanding neural networks and their applications. If you need that, you will want to augment this book with writings that cover some of the other network architectures (SOM, hopfield, etc) that are out there. The same goes for the other topics that it covers.Read more ›
My favorite part is how he shows us code (gives it to us!) that goes out into the world, grabs masses of data and does interesting things with it. The use of a hierarchical clustering algorithm to dig into people's intrinsic desires in life as expressed in zebo is worth the price of the book alone. The graph that shows a strong connection between "wife", "kids", and "home" but a different connection between "husband", "children", and "job" is IMHO just fascinating.
Gems like that make this book worth reading cover to cover. After that it can happily hang out on your shelf as a reference anytime you need to build something to mine user data and extract the wisdom of crowds.
Introduction to Collective Intelligence; Making Recommendations; Discovering Groups; Searching and Ranking; Optimization; Document Filtering; Modeling with Decision Trees; Building Price Models; Advanced Classification - Kernel Methods and SVMs; Finding Independent Features; Evolving Intelligence; Algorithm Summary; Third-Party Libraries; Mathematical Formulas; Index
In each of the chapters, Segaran takes a type of capability, be it decision-making or filtering, and shows how a programming language can be used to build that feature. His examples are all in Python, so it helps if you are already familiar with that language if you want to actually work with the code. But even if you don't know Python, the examples are clear and detailed enough that you can follow along and get the gist of what's happening. I personally think that it would help immensely if you had a background in mathematics and statistics. You can use the code here without having a detailed understanding of math, but I'm sure much of this would be more deeply appreciated if you already know about such things as Tanimoto similarity scores, Euclidean distances, or Pearson coefficients.Read more ›
Most Recent Customer Reviews
This book is garbage. There are A LOT of links in it to websites that contain information to follow along with the examples in the book. NONE of the links still work. Read morePublished 5 months ago by Jesse McDiz
It does a good job giving an overview of how things work and in that sense it's a pretty instructive book. However, when digging into the code it can often be frustrating. Read morePublished 5 months ago by Joshua Cole
The content of this book is very good. The exercises are very helpful to solidify understanding and explore topics beyond the material in a chapter. Read morePublished 6 months ago by Young Investor
Good to the Machine Learning algorithms. I specially like the summarized set of all algorithms at the end of the book!Published 7 months ago by George
What a great book. Good Job. Minor code updates available.
Beats many newer Python Machine Learning HowTo books.
Have a lot of techniques, however is not easy to apply to real world examples, I think is a complementary reading for after you learn some of the basics of machine learning and... Read morePublished 14 months ago by Franco Risso
Most of the part of this book are code examples. It contains a lot of typo in the code example, thus some code doesn't work at all. Read morePublished 14 months ago by s0ra