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。
评分都是干货,没什么废话。注重由浅入深向读者讲解,兼顾各种细节。作者的编程经验丰富,书里的代码都是选自案例,可以直接应用。所以,这本书特别实用。 对我来说,终于搞明白了一种神经网络:多层感知机。首先将抽象神经元的权重(突触强度)存入到数据库中,或者通过反向传播...
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 评分据说是入门的...
评分豆瓣的由来。。
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。
评分觉得应该给三星半。结构内容是不错,只是API各种过期,例如geocoding的那个。书上代码有问题的地方也不少。
评分很不错的应用+理论书,可惜就是有些api没法用
本站所有内容均为互联网搜索引擎提供的公开搜索信息,本站不存储任何数据与内容,任何内容与数据均与本站无关,如有需要请联系相关搜索引擎包括但不限于百度,google,bing,sogou 等
© 2026 book.wenda123.org All Rights Reserved. 图书目录大全 版权所有