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Algorithms of the Intelligent Web 1st Edition
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I have read the book front-to-back (twice!) before I write this report. I started reading the electronic version a couple of months ago and read the paper print again over the weekend. This is the best practical book in machine learning that you can buy today -- period. All the examples are written in Java and all algorithms are explained in plain English. The writing style is superb! The book was written by one author (Marmanis) while the other one (Babenko) contributed in the source code, so there are no gaps in the narrative; it is engaging, pleasant, and fluent. The author leads the reader from the very introductory concepts to some fairly advanced topics. Some of the topics are covered in the book and some are left as an exercise at the end of each chapter (there is a "To Do" section, which was a wonderful idea!). I did not like some of the figures (they were probably made by the authors not an artist) but this was only a minor aesthetic inconvenience.
The book covers four cornerstones of machine learning and intelligence, i.e. intelligent search, recommendations, clustering, and classification. It also covers a subject that today you can find only in the academic literature, i.e. combination techniques. Combination techniques are very powerful and although the author presents the techniques in the context of classifiers, it is clear that the same can be done for ecommendations -- as the Bell Korr team did for the Netflix prize.Read more ›
First of all, the code uses BeanShell as a way to run the examples. BeanShell is a neat idea. It's one of a number of languages that move Java closer to being a scripting language. But it's not necessary for the book's purposes. It's a bit of a pain to install, and it takes a while to get used to. In the end it's an unnecessary distraction. It's far simpler to run the examples in eclipse with the "scripts" entered as the body of a main() method.
The preceding is a relatively minor point, but in some ways it illustrates some of the problems I had with the book. It focuses too much on the code. Yes, it's nice to have code that does what one is trying to describe, but code is not a substitute for a good explanation. In many places the book provides inadequate descriptions of the concepts, presumably on the grounds that one can just read the code. But code is not tutorial. Code itself must be commented to be understandable. And code cannot replace a good intuitive description of the important ideas.
Furthermore, the code (and the output) take up too much space in the book. There are pages of output when a few lines would suffice, and there are pages of code when a well-constructed paragraph would do. Pearson's coefficient is a good example. There is approximately a page of code to do the calucuation. There is also half a page of code-level comments--e.g., "The method getAverage is self-explanatory; it calculates the average of the vector that's provided as an argument.Read more ›
The author is attempting to teach both the algorithms behind the information retrieval that is done on the web and at the same time show those algorithms implemented in Java in such a way that it is clear to the reader what has been done. This approach can be a tricky middle ground often resulting in books that are confusing from both a textbook and from a cookbook standpoint. Fortunately, the author has done a good job of integrating these two viewpoints into a cohesive whole and the result is a book I can heartily recommend. The author makes liberal use of figures and explains what is being done at a high level first, showing pseudocode before actually showing the Java code. Discussions on the inner workings of the algorithms follow.
Note that use is made of higher level libraries such as Lucene when they are available, because this is a book for professionals after all, and your boss would not be pleased if you reinvented the wheel every time you implemented an algorithm. But, don't worry, the explanation behind the code is there too. Another good book that is language agnostic that makes a good companion to this one is ...Read more ›
Most Recent Customer Reviews
Each chapter introduces a broad topic including searching, recommendations, clustering and classification and presents a good review of several alternative techniques/algorithms to... Read morePublished 16 months ago by Paolo Baronti
Really nice book, a lot of useful material on data mining algorithms.Published 17 months ago by Ievgen Shchepetov
It is rear these days to find a book that gives you both, general overview and the detail description of technologies but this particular one does just that. Read morePublished on October 18, 2013 by OBender
This was a good book for giving an overview of algorithms to consider for the 'intelligent web'. It was let down by the convoluted descriptions of how the algorithms worked,... Read morePublished on August 17, 2013 by Amazon Customer
This book was my introduction to machine learning after many years in aerospace and radar. It allowed me to ramp up and become productive in this new field. Read morePublished on October 24, 2012 by Andrew
First and foremost, congratulations for authors for such a high quality text and narrative style which artfully slaloms between pop and sci. Read morePublished on September 15, 2012 by Srecko Gnjidic
Artificial intelligence books are usually very aesthetic in deep thought and mathematics. This book is different: it's a fast and astonishingly easy "hands on" introduction. Read morePublished on June 15, 2011 by ws__
This is a book that comes from an author whom I know personally and worked with.
A superb book by its own - which comes from an author who is an authority on the subject... Read more