Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: * 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 a dataset * 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
Toby Segaran works as a Data Magnate at Metaweb Technologies. Prior to working at Metaweb, he started a biotech software company called Incellico which was later acquired by Genstruct. His book, "Programming Collective Intelligence" has been the best-selling AI book on Amazon for several months. He is the recipient of a National Interest Waiver for "People of Exceptional Ability", and currently lives in San Francisco. His blog and other information are located at kiwitobes.com.
这部书写的非常好,如果与机器学习课程结合起来看的话会起到事半功倍的效果。此书重于实践,从源代码中也能看懂各章的知识,可以说,读了此书,会对人工智能有个更深入的认识。
评分中国有句老话,叫做“知易行难”。 作算法的朋友应该更有体会,想把 paper 上的公式转变为可以运行的代码,这是件考验功力的事情。 Toby Segaran 写的这本《Programming Collective Intelligence》,是修炼此种功力的武林秘笈之一。 这本书最显著的特点是,实战性极强! 针对...
评分可能不是什么最新的研究热点 不过就读完第一章之后来看,基本上验证了我之前对于协同过滤方面的知识,并且感觉可以作为后续研究的一个指导和激励。 看到后面的章节内容,支持向量机,神经网络等之前在工程上用的少之又少的东西都能有它们的用武之地,让人相当之兴奋。 其实目前...
评分上周50周年系庆的时候 张钹 院士说了这样一句话:”人工智能以前大多基于经验和领域知识,直到上万上亿的数据出现时,基于数据的人工智能更有了广阔的天空。”《集体智慧》就是这样一本告诉你如何从数据中挖掘金矿的经典之作。 由于现在所从事的是信息检索,文本挖掘方面的研究...
评分好书,介绍一些常用算法的使用方法,如神经网络,支持向量机,模拟退火,遗传算法等.对普通读者已经够了.能将这些算法用熟,就能开发出非常好的应用程序来。 缺少当今最流行的小波和独立分量分析,当然不可能有最新的变分贝叶斯理论.对研究算法且用于应用开发的人来说可以是一个好实...
100年前读的
评分just open your eyes in this area, not the best solutions from trench.
评分每章都是实例,实用性很强,基本的机器学习的方法都有涉及(regression涉及较少),只是代码一点儿没有pandas, sklearn, scipy, nltk等包,numpy也只是用了一下而已,不免有些过时,所以从实用性而言又打了一些折扣,但对于理解算法的原理却比直接用package要好许多。
评分机器学习入门好书,实践导向
评分上个学期花了200多买的原版书,确实太浅了,读的时候还发现几处代码错误= =,当入门书不错
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