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This library provides a pure python interface for the Twitter API.

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SVDLIBC is a C library based on the SVDPACKC library, which was written by Michael Berry, Theresa Do, Gavin O'Brien, Vijay Krishna and Sowmini Varadhan. SVDLIBC offers a cleaned-up version of the code with a sane library interface and a front-end executable that performs matrix file type conversions, along with computing singular value decompositions. Currently the only SVDPACKC algorithm implemented in SVDLIBC is las2, because it seems to be consistently the fastest. This algorithm has the drawback that the low order singular values may be relatively imprecise, but that is not a problem for most users who only want the higher-order values or who can tolerate some imprecision.

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recommender programming

FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search and a system for automatically choosing the best algorithm and optimum parameters depending on the dataset.

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python recommender programming

Divisi uses a sparse higher-order SVD can help find related concepts, features, and relation types in any knowledge base that can be represented as a semantic network. By including common sense knowledge from ConceptNet, the results can include relationships not expressed in the original data but related by common sense.

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rpy2 is a redesign and rewrite of rpy. It is providing a low-level interface to R, a proposed high-level interface, including wrappers to graphical libraries, as well as R-like structures and functions.

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Go is an open source project, distributed under a BSD-style license. This document explains how to check out the sources, build them on your own machine, and run them.

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This is the UI specification for the default keymap profile in the NetBeans IDE 6.0, 6.1 and 6.5. The "old" NetBeans 5.5 profile is still available in Tools | Options | Keymap | Profile.

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The PySide project provides LGPL-licensed Python bindings for the Qt cross-platform application and UI framework. PySide Qt bindings allow both free open source and proprietary software development and ultimately aim to support all of the platforms as Qt itself.

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Cascading is a feature rich API for defining and executing complex, scale-free, and fault tolerant data processing workflows on a Hadoop cluster.

The processing API lets the developer quickly assemble complex distributed processes without having to "think" in MapReduce. And to efficiently schedule them based on their dependencies and other available meta-data. Obviously simple data processing applications are supported as well, as complex jobs tend to start simple.

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MapReduce machine learning data mining programming

Part of what makes Google such an amazing engine of innovation is their internal technology stack: a set of powerful proprietary technologies that makes it easy for Google developers to generate and process enormous quantities of data. According to a senior Microsoft developer who moved to Google, Googlers work and think at a higher level of abstraction than do developers at many other companies, including Microsoft: “Google uses Bayesian filtering the way Microsoft uses the if statement” (Credit: Joel Spolsky). This series of posts describes some of the technologies that make this high level of abstraction possible. 

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SVMpython is a Python embedded version of SVMstruct. One applies SVMstruct by modifying the svm_struct_api.c file and recompiling. SVMpython allows one to write these functions in Python instead: one applies SVMpython by creating a Python module (commonly just a .py file) with the appropriate methods. This module is loaded and specific methods called at runtime to support the structural learning algorithm.

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Once the exclusive domain of academics and corporations with large research budgets, intelligent applications that learn from data and user input are becoming more common. The need for machine-learning techniques like clustering, collaborative filtering, and categorization has never been greater, be it for finding commonalities among large groups of people or automatically tagging large volumes of Web content. The Apache Mahout project aims to make building intelligent applications easier and faster. Mahout co-founder Grant Ingersoll introduces the basic concepts of machine learning and then demonstrates how to use Mahout to cluster documents, make recommendations, and organize content.

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