Amazon Price: N/A (as of November 22, 2017 01:44 –
With the proliferation of the web, there has never been a larger body of data freely available for common use. Harvesting and processing this data can be a time consuming task if done manually. However, web scraping can provide the tools and framework to accomplish this with the click of a button. It's no wonder, then, that web scraping is a desirable weapon in any programmer's arsenal.
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Amazon Price: N/A (as of November 23, 2017 00:25 –
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 mediaMethods of clustering to detect groups of similar items in a large datasetSearch engine features — crawlers, indexers, query engines, and the PageRank algorithmOptimization algorithms that search millions of possible solutions to a problem and choose the best oneBayesian filtering, used in spam filters for classifying documents based on word types and other featuresUsing decision trees not only to make predictions, but to model the way decisions are madePredicting numerical values rather than classifications to build price modelsSupport vector machines to match people in online dating sitesNon-negative matrix factorization to find the independent features in a datasetEvolving intelligence for problem solving — how a computer develops its skill by improving its own code the more it plays a gameEach 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.
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Amazon Price: N/A (as of November 23, 2017 00:25 –
Innovations in hardware architecture, like hyper-threading or multicore processors, mean that parallel computing resources are available for inexpensive desktop computers. In only a few years, many standard software products will be based on concepts of parallel programming implemented on such hardware, and the range of applications will be much broader than that of scientific computing, up to now the main application area for parallel computing.
Rauber and Rünger take up these recent developments in processor architecture by giving detailed descriptions of parallel programming techniques that are necessary for developing efficient programs for multicore processors as well as for parallel cluster systems and supercomputers. Their book is structured in three main parts, covering all areas of parallel computing: the architecture of parallel systems, parallel programming models and environments, and the implementation of efficient application algorithms. The emphasis lies on parallel programming techniques needed for different architectures. For this second edition, all chapters have been carefully revised. The chapter on architecture of parallel systems has been updated considerably, with a greater emphasis on the architecture of multicore systems and adding new material on the latest developments in computer architecture. Lastly, a completely new chapter on general-purpose GPUs and the corresponding programming techniques has been added.
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Amazon Price: N/A (as of November 22, 2017 07:31 –
Mit BOINC können komplexe und rechenintensive Probleme im Sinne des Public Resource Computing gelöst werden: Freiwillige melden sich bei einem BOINC-Projekt an und stellen ihre Rechnerressourcen zur Verfügung. Der Autor erläutert die einzelnen Entwicklungsschritte, so dass ein voll funktionsfähiges BOINC-Projekt erstellt und effizient gewartet werden kann. Komplexe Zusammenhänge werden anhand der Unified Modeling Language (UML) veranschaulicht. Mit detaillierten Beschreibungen der Programmierschnittstelle und praxisnahen Fallbeispielen.
Amazon Price: $39.99 $33.82 You save: $6.17 (15%). (as of November 22, 2017 15:19 –
Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis
Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power.
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