Showing posts with label algorithms. Show all posts
Showing posts with label algorithms. Show all posts

An Introduction to MultiAgent Systems Review

An Introduction to MultiAgent Systems
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An Introduction to MultiAgent Systems ReviewThis is a great book. I am a Computer Science Grad Student and this book is a great intro book. It is not only a great read and easily understandable (ie. it is written for undergrad to Grad) but it also has a moderately high degree of technicality that makes it more interesting to the more advanced reader. I would definitely recommend this book to anyone interested in learning more about the subject of MultiAgent Systems.An Introduction to MultiAgent Systems OverviewThe eagerly anticipated updated resource on one of the most important areas of research and development: multi-agent systems

Multi-agent systems allow many intelligent agents to interact with each other, and this field of study has advanced at a rapid pace since the publication of the first edition of this book, which was nearly a decade ago. With this exciting new edition, the coverage of multi-agents is completely updated to include several areas that have come to prominence in the last several years, including auctions, computational social choice, and markov decision processes. In turn, a variety of topics that were initially considered critical have dwindled in importance, so the coverage of that subject matter is decreased with this new edition. The result of this redefined balance of coverage is a timely and essential resource on a popular topic.

Introduces you to the concept of agents and multi-agent systems and the main applications for which they are appropriate
Discusses the main issues surrounding the design of intelligent agents and a multi-agent society
Delves into a number of typical applications for agent technology
Addresses deductive reasoning agents, practical reasoning agents, reactive and hybrid agents, and more
Reviews multi-agent decision making, communication and cooperation, and intelligent autonomous agents

By the end of the book, you will have a firm grasp on how agents are distinct from other software paradigms and understand the characteristics of applications that lend themselves to agent-oriented software.


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Beautiful Code: Leading Programmers Explain How They Think (Theory in Practice (O'Reilly)) Review

Beautiful Code: Leading Programmers Explain How They Think (Theory in Practice (O'Reilly))
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Beautiful Code: Leading Programmers Explain How They Think (Theory in Practice (O'Reilly)) ReviewThe idea of this book is that thirty software developers and/or researchers (respectable ones, no doubt there), had to find the most beautiful piece of code and present its study. Each of them then writes a chapter and there you have it - a volume of "beautiful code" ! Simple as that.
If there was somebody to fully support the idea of such book, it would be me - I believe that the software industry already spent too much time and effort neglecting the art-and-craft in programming, pretending that it all can be reduced to hard math. Didn't work so far, did it ? Then I very welcome books like this one. But not exactly the one.
Let me put it this way - I couldn't say anything good about this book except that I adore the concept and found may be ten of thirty three chapters interesting (not necessarily beautiful). Beauty is in the eye of the beholder they say, but this lame excuse is the last good thing I could say for this book.
It was supposed to be pedagogical. Did not happen. Rather than making it timeless reference for the readers, the book made a tribune for the authors to talk about, uhm, just about anything. We know how programmers love to talk about what they do, and it's ok. But we also know that they often mumble instead of talking and it's very difficult for us to understand one another, no matter friendly or hostile. This is not to mention that there are no commonality in topics or style or language (programming or English) or anything. The editor had simply glued it together.
Not so bad you say, a good assortment is fine you say ? Let me tell you more, and it's all downhill.
It's as though you expected an album of paintings but instead got a book of random excerpts from chemical specifications for producing paints.
Exemplary conventional antimicrobial, antimildew, or antialgae agent includes 3-iodo-2-propynyl butylcarbamate, diiodomethyl-p-tolylsulfone, 1,2-benzoisothiazolin-3-one, 2-methylthio-4-tert-butylamino-6-cyclopropylamino-s-triazine, 2-(4-thiocyanomethylthio) benzothiazole and the mixtures thereof.
See how beautiful it is that can be painted with that ?
If you ask me, a book like this ought to have structure. Remember the classic one by Gamma et al - they also presented abstract things from different areas or levels, but they kept the information stylistically uniform and structured against a clear taxonomy. Not the case here.
Each chapter is about different matter, presented in a different way. One author presents a performance hack in which he compiles code on the fly. The chapter will then contain several pages of dynamic assembly. The other will show an interesting approach to syntax parsing. This one will have 50 short snippets of something JavaScript-like. Yet another will tell you how to automate debugging by automatically mutating the application. This one won't have code at all. Yet another will show a slick algorithm for counting bits in a word. This one will have a lot of bitwise arithmetic.
And I just loved the one that has NASA in it's title. There - "A Highly Reliable Enterprise System For NASA's Mars Rover Mission". Wow ! How promising ! Want to know what it says ? It says - "In NASA they love their software reliable, even a web-based file server, and so we present you a web-based file server built with JavaBeans in three-tier architecture". Ahem, Mars Rover anyone ?
Don't get me wrong, some of the chapters are reasonably interesting. Interesting ! Not beautiful !
With a little exception, the authors don't even mention the word "beautiful" in their texts. They allure with "There, we have this system, it works like this..." . What exactly the author finds beatiful about it and why - remains secret.
The most impressive standout was the chapter written by Yukihiro Matsumoto, the creator of Ruby. Three pages in which he simply speaks about what he believes a beautiful code is. He explains to you his understanding of a beautiful code. This is what the book is all about !
Instead, many chapters just demonstrate a few pages (!) of code and conclude - it is beautiful, see !
Many times I wasn't unable to grasp the problem - what was it that required that so called beauty to emerge ? I couldn't see the whole picture, but the authors sort of presume I do and so my possible appreciation of beauty requires deep understanding. What if I show you a magnified fragment of Mona Lisa's background, some 3x3 blackish pixels ? No doubt, Leonardo had to paint them too. But what was that beauty again ?
Only a few authors were wise enough to use a pseudocode. Something that anyone can read, no matter from which camp. Otherwise it's just weird when the authors present their beatiful code in Ruby or Perl or LISP. Look, I didn't touch Ruby yet, I hate Perl and I can't imagine using LISP in practice. Nevertheless the authors repeatedly say something like "It's easy, I'll show you, this bracket does this and that character does something else. Now you see how beautiful it is ?". They literally show you a piece of poetry in foreign language and ask you to appreciate it.
A classical example of awful poetry in Russian is (transliterated)
Ya poet, zovus' Neznajka,
ot menya vam balalajka.
Can you tell whether it's good or bad and why ? What if I told you it's beatiful ? Would you believe ? Does it appeal to your sense of beauty ? Same thing about this entire book.
Awful implementation of an idea that I fully adore. In fact, implementations like this undermine the idea, that's why I rate this book so low and put it away with disgust.
Beautiful Code: Leading Programmers Explain How They Think (Theory in Practice (O'Reilly)) OverviewHow do the experts solve difficult problems in software development? In this unique and insightful book, leading computer scientists offer case studies that reveal how they found unusual, carefully designed solutions to high-profile projects. You will be able to look over the shoulder of major coding and design experts to see problems through their eyes. This is not simply another design patterns book, or another software engineering treatise on the right and wrong way to do things. The authors think aloud as they work through their project's architecture, the tradeoffs made in its construction, and when it was important to break rules. Beautiful Code is an opportunity for master coders to tell their story. All author royalties will be donated to Amnesty International. tion.

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Programming Collective Intelligence: Building Smart Web 2.0 Applications Review

Programming Collective Intelligence: Building Smart Web 2.0 Applications
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Programming Collective Intelligence: Building Smart Web 2.0 Applications ReviewThis book is probably best for those of you who have read the theory, but are not quite sure how to turn that theory into something useful. Or for those who simply hunger for a survey of how machine learning can be applied to the web, and need a non-mathematical introduction.
My area of strength happens to be neural networks (my MS thesis topic was in the subject), so I will focus on that. In a few pages of the book, the author describes how the most popular of all neural networks, backpropagation, can be used to map a set of search terms to a URL. One might do this, for example, to try and find the page best matching the search terms. Instead of doing what nearly all other authors will do, prove the math behind the backprop training algorithm, he instead mentions what it does, and goes on to present python code that implements the stated goal.
The upside of the approach is clear -- if you know the theory of neural networks, and are not sure how to apply it (or want to see an example of how it can be applied), then this book is great for that. His example of adaptively training a backprop net using only a subset of the nodes in the network was interesting, and I learned from it. Given all the reading I have done over the years on the subject, that was a bit of a surprise for me.
However, don't take this book as being the "end all, be all" for understanding neural networks and their applications. If you need that, you will want to augment this book with writings that cover some of the other network architectures (SOM, hopfield, etc) that are out there. The same goes for the other topics that it covers.
In the end, this book is a great introduction to what is available for those new to machine learning, and shows better than any other book how it applies to Web 2.0. Major strengths of this book are its broad coverage, and the practicality of its contents. It is a great book for those who are struggling with the theory, and/or those who need to see an example of how the theory can be applied in a concise, practical way.
To the author: I expect this book will get a second edition, as the premise behind the book is such a good one. If that happens, perhaps beef up the equations a bit in the appendix, and cite some references or a bibliography for those readers interested in some more in depth reading about the theory behind all these wonderful techniques. (The lack of a bibliography is why I gave it 4 stars out of 5, I really think that those who are new to the subject would benefit greatly from knowing what sits on your bookshelf.)Programming Collective Intelligence: Building Smart Web 2.0 Applications OverviewWant 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

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Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems) Review

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems) ReviewI'm surprisingly please with this book. I've been reading up on the topic and associated algorithms in other books for some time; I'm a software developer but don't have a statistics background, and so felt a lot of the texts were too focused on the math and the theory while being thin on content when it came to "rubber hitting the road", or even using clear, simple examples and straight-forward notation.
This book is so well-written that it communicates the concepts clearly, lucidly and in an organized fashion. The section that introduces Bayesian probability was drop-dead simple to follow. Quite frankly, having read a few other treatments on it, I can now say that everything else I read before this was overly complicated. Brevity is the soul of wit, no?
To the reviewer who criticized the authors use of words to describe equations: This is what the authors intended to do. Would you fault them for writing in English if you wanted Greek? Not everyone who can benefit from applied data mining has the requisite background to understand the nitty gritty mathematics, nor should they have to, if they just want to understand the behavior and practical applications of the technology.Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems) Overview

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Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites Review

Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites
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Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites ReviewMining the Social Web does a great job of introducing a wide variety of techniques and wealth of resources for exploring freely available social data and personal information. If you are willing to spend the time tinkering with the examples, the book is pure fun. It offers a nice compliment to Segaran's Programming Collective Intelligence: Building Smart Web 2.0 Applications. The two books overlap but where they do offer different perspectives and explanations of common techniques (e.g., TF-IDF, cosine similarity, Jaccard index). If you are well-versed in data mining the web you may find much of the discussion familiar. If you have only been casually engaged to date, your toolbox will fill quickly.
In order to work with the book's examples related to LinkedIn and Facebook you really need to have a robust collection of connections. In terms of the source code itself, most of it worked as is. I wasn't able to install the Buzz library which limited my interaction with material in chapter 7 and opted to not get involved with the LinkedIn or Facebook but found the discussions around them easy to follow. By far my favorite chapter in the book was chapter 8, "Blogs et al.: Natural Language Processing (and Beyond)..." It was quite fascinating and caused my reading list to grow considerably.Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites Overview
Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data, but how can you find out who's making connections with social media, what they're talking about, or where they're located? This concise and practical book shows you how to answer these questions and more. You'll learn how to combine social web data, analysis techniques, and visualization to help you find what you've been looking for in the social haystack, as well as useful information you didn't know existed.

Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools.

Get a straightforward synopsis of the social web landscape
Use adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, and LinkedIn
Learn how to employ easy-to-use Python tools to slice and dice the data you collect
Explore social connections in microformats with the XHTML Friends Network
Apply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection
Build interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits

"Let Matthew Russell serve as your guide to working with social data sets old (email, blogs) and new (Twitter, LinkedIn, Facebook). Mining the Social Web is a natural successor to Programming Collective Intelligence: a practical, hands-on approach to hacking on data from the social Web with Python." --Jeff Hammerbacher, Chief Scientist, Cloudera

"A rich, compact, useful, practical introduction to a galaxy of tools, techniques, and theories for exploring structured and unstructured data." --Alex Martelli, Senior Staff Engineer, Google


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