Programming Collective Intelligence: Building Smart Web 2.0 Applications Book Description:
- Collaborative filtering techniques that enable online retailers to recommend products or media
- Methods of clustering to detect groups of similar items in a large dataset
- Search engine features–crawlers, indexers, query engines, and the PageRank algorithm
- Optimization algorithms that search millions of possible solutions to a problem and choose the best one
- Bayesian filtering, used in spam filters for classifying documents based on word types and other features
- Using decision trees not only to make predictions, but to model the way decisions are made
- Predicting numerical values rather than classifications to build price models
- Support vector machines to match people in online dating sites
- Non-negative matrix factorization to find the independent features in adataset
- Evolving intelligence for problem solving–how a computer develops its skill by improving its own code the more it plays a game
Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you.
“Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details.”
— Dan Russell, Google
“Toby’s book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths.”
— Tim Wolters, CTO, Collective Intellect
Publisher:O’Reilly Media; 1st edition (September 11, 2007)
Item Weight:1.27 pounds
Dimensions:7 x 0.9 x 9.19 inches
Programming Collective Intelligence: Building Smart Web 2.0 Applications
(as of Oct 23,2021 21:10:54 UTC )
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