|
|
Title: Programming Collective Intelligence: Building Smart Web 2.0 Applications
Purchase
Item
Manufacturer: O'Reilly Media, Inc.
List Price: $39.99
Our Price: $21.53
|
|
| Customer Reviews: |
| Programming Collective Intelligence: Building Smart Web 2.0 Applications by O'Reilly Media, Inc. Excellent Resource for Clustering Algorithoms and Other AI Algorithoms | | I use python as my primary programming language, when I ordered this book I was concerned it would be more about website design then AI algorithms (collective intelligence encompasses a subset of soft AI algorithms that draw upon information from various sources readily avaliable on the Internet, large document collections, etc.) I found the text to be readable with broad application in other areas including document classification systems for analyzing large amount of documents in the context of e-discovery. I would recommend this book to anyone using any-type of clustering process for review and analyzing documents and data. Taxonomic, clustering, neural networks, etc. are sold generally to the public as magic while in fact the concepts are readily accessible in this book. | | Programming Collective Intelligence: Building Smart Web 2.0 Applications by O'Reilly Media, Inc. Great working examples | Love the book, great topical review of methods with working examples. Every chapter makes you think of a dozen things you could do next.
My only reason for 4 instead of 5 stars is that the code examples are all python-based and leverage python specific features. The book title should be "Programming Collective Intelligence...with Python" although it does present a fun challenge to convert the examples to a different language (like Ruby!). | | Programming Collective Intelligence: Building Smart Web 2.0 Applications by O'Reilly Media, Inc. useable math in the web 2.0 sphere | | This book introduces you to a lot of useful math for web 2.0 or social based applications and brings it all the way down to code you can write and run in Python. I learned about some great python libraries out there like beautiful soup and others which are useful in more ways than just the collective intelligence aspect utilized in the book. There were even a few more elegant ways of doing something in Python that I learned through reading the code in this book. Just about every application I use could make use of the math and algorithms in this book to make using it a bit more pleasant experience. If you're a python programmer you must have this on your bookshelf, if you are a programmer that wants people to like your application you should have this book in a tattered state on your bookshelf. | | Programming Collective Intelligence: Building Smart Web 2.0 Applications by O'Reilly Media, Inc. Fantastic Intro to Machine Learning for Software Engineers | Have you ever wondered how:
* Google comes up with its search results
* Amazon recommends you books/movies/music
* spam filters decide good from bad
Well, Toby Segaran not only explains these topics and more in Collective Intelligence, but he does so in a way accessible to software developers that haven't worked on machine-learning problems before. He even provides working Python code for all the algorithms.
Collective Intelligence is a great read. I could not wait to get home and get back to it -- and when I went in to work the next morning, I usually had a new idea or two of how to improve our software. I also have been implementing the most important examples in Groovy to make sure I get them.
Collective Intelligence is accessible to all practicing software engineers, but if you are a Senior Software Engineer or "better," this is a must-read. Proper application of the algorithms in this book are a great way to simplify your system and avoid getting nickel-and-dimed to death with new ways to prioritize/categorize/slice-and-dice your data. | | Programming Collective Intelligence: Building Smart Web 2.0 Applications by O'Reilly Media, Inc. Algorithms By Example | I've worked in web development for years now. I get excited when I see new web trends and applications come out, and I love the progress we've made with mashups and the like. It's great to see what the web has become.
I picked this book up because all the examples were in Python, and I'm a big fan of python. I also liked the concept of writing mashups in Python. I expected it to be very python-centric. It was, but that wasn't what stuck out to me.
What I found was a book all about algorithms. I've been fascinated with some of the algorithms we see every day on the internet (Amazon's suggestion algorithm has been my favorite). Instead of presenting confusing math equations, or using huge words, Segaran puts examples in front of you. From online dating services, to del.icio.us trends, this book puts forward modern, real world examples of using common collective intelligence algorithms on the internet.
Anyone interested in building a mashup or web development in general should read this book. | | Programming Collective Intelligence: Building Smart Web 2.0 Applications by O'Reilly Media, Inc. Product Description | | 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 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 |
| |
