Programming Collective Intelligence

Toby Segaran

出版时间

2007-08-26

ISBN

9780596529321

评分

★★★★★
书籍介绍
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
AI导读
核心看点
  • 以Python代码实战解析机器学习算法
  • 涵盖协同过滤、聚类、分类等核心技术
  • 结合Web 2.0场景挖掘用户行为数据
适合谁读
  • 具备Python基础的数据挖掘初学者
  • 希望将算法落地到Web应用的开发者
  • 对人工智能原理感兴趣的非数学专家
读前提醒
  • 书中部分API已过期,需自行替换接口
  • 代码实现较为基础,不建议直接用于生产
  • 建议结合现代机器学习框架对比学习
读者共识
  • 实例丰富直观,非常适合入门学习
  • 数学公式讲解通俗,降低理解门槛
  • 代码质量一般,重在理解算法思想

本导读基于书籍简介、目录、原文摘录、短评和书评生成,不等同于全文精读。

精彩摘录
  • "Next, get a list of random people to make up the dataset. Fortunately, Hot or Not provides an API call that returns a list of people with specified criteria. In this exam- ple, the only criteria will be that the people have “meet me” profiles, since only from these profiles can you get other informa"
  • "What Does This Have to Do with the Articles Matrix? So far, what you have is a matrix of articles with word counts. The goal is to factorize this matrix, which means finding two smaller matrices that can be multiplied together to reconstruct this one. The two smaller matrices are: The features matri"
  • "Another feature that applies more evenly to a couple of companies is this one: Feature 2 (46151801.813632453, 'GOOG') (24298994.720555616, 'YHOO') (10606419.91092159, 'PG') (7711296.6887903402, 'CVX') (4711899.0067871698, 'BIIB') (4423180.7694432881, 'XOM') (3430492.5096612777, 'DNA') (2882726.88776"
  • "Because new connections are only created when necessary, this method has to return a default value if there are no connections. For links from words to the hidden layer, the default value will be –0.2 so that, by default, extra words will have a slightly negative effect on the activation level of a "
  • "Pearson Correlation Score A slightly more sophisticated way to determine the similarity between people’s inter- ests is to use a Pearson correlation coefficient. The correlation coefficient is a mea- sure of how well two sets of data fit on a straight line. The formula for this is more complicated t"
  • "Simulated annealing is an optimization method inspired by physics. Annealing is the process of heating up an alloy and then cooling it down slowly. Because the atoms are first made to jump around a lot and then gradually settle into a low energy state, the atoms can find a low energy configuration."
  • "The flight scheduling example works because moving a person from the second to the third flight of the day would probably change the overall cost by a smaller amount than moving that person to the eighth flight of the day would. If the flights were in random order, the optimization methods would wor"
  • "Squaring the numbers is common practice because it makes large differences count for even more. This means an algorithm that is very close most of the time but far off occasionally will fare worse than an algorithm that is always somewhat close. This is often desired behavior, but there are situatio"
作者简介
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.
用户评论
第一次看用代码表示数学公式的书,有一种码农造反的感觉。。
pdf, 读的不算太仔细. 不过入门应该没问题了.
机器学习入门好书,实践导向
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。
实例教程,可作工具书,经常查阅。在线阅读地址:http://www.docin.com/p-47422429.html
开拓视野
分析到位,内容有点旧。
涵盖了一些常见的数据挖掘算法,和内容对比书名似乎有点容易误导,除非说只要是数据集都算collective。各种算法蜻蜓点水,这次算是借本书为索引,再去搜索、巩固一些基本知识。一些应用试用场景及细节讲得不错,但是没有对应上专有术语也没有引用,导致入门者难以深入。代码和实验没怎么看得进去(genetic programming那章实现除外)。随机优化那一章收获蛮多的:cost function, representation of constrained problem, similar solutions yield similar results。
原理讲得非常清楚明白,但是书中很多例子和代码都已经过时了
这种蠢书评价这么高,看来真的是阿猫阿狗也说自己在搞机器学习
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