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Visual Understanding Environment
The Visual Understanding Environment (VUE) is an Open Source project based at Tufts University. The VUE project is focused on creating flexible tools for managing and integrating digital resources in support of teaching, learning and research. VUE provides a flexible visual environment for structuring, presenting, and sharing digital information.
Welcome :: Greenstone Digital Library Software
Greenstone is a suite of software for building and distributing digital library collections. It provides a new way of organizing information and publishing it on the Internet or on CD-ROM. Greenstone is produced by the New Zealand Digital Library Project at the University of Waikato, and developed and distributed in cooperation with UNESCO and the Human Info NGO. It is open-source, multilingual software, issued under the terms of the GNU General Public License. Read the Greenstone Factsheet for more information.
Software Carpentry: July 2009 Guest Speakers
What Every Scientist Needs to Know About
How the Web is Changing the Way They Work
City Invites Software Developers to Crunch Big Data Sets - City Room Blog - NYTimes.com
In what is planned to become an annual competition known as NYC Big Apps, the city will make available about 80 data sets from 32 city agencies and commissions. The winners of the competition will get a cash prize, recognition at a dinner with the mayor, and marketing opportunities.
Bundler - Structure from Motion for Unordered Image Collections
Bundler is a structure-from-motion system for unordered image collections (for instance, images from the Internet).
Enterprise 2.0 Software: Commoditization before Monetization | Pretzel Logic - Enterprise 2.0
So assuming that the software stack continues down this path to commoditization, what’s left to make money on? Directory Integration? Application Integration? Change Management Consulting?
VFML
Welcome to the VFML (Very Fast Machine Learning) toolkit for mining high-speed data streams and very large data sets. VFML is made up of three main components. The first is a collection of tools and APIs that help a user develop new learning algorithms. The second component is a collection of implementations of important learning algorithms. The third component is a collection of scalable learning algorithms that were developed by Pedro Domingos and Geoff Hulten (with the help of several other people
README.md at master from brendano's tsvutils - GitHub
so far I've found that TSV-with-headers is the most convenient canonical format for table data in the filesystem/commandline environment. It's also good as a lingua franca intermediate format in shell pipelines. These utilities are just a little bit of glue to make TSV play nicely with Excel, MySQL, and Unix tools. Interfaces in and out of other table-centric environments could easily be added.
openbiomind - Google Code
OpenBiomind is a toolkit for analysis of gene expression, SNP and other biological datasets using advanced machine learning and pattern mining techniques, and includes traditional clustering, hybrid clustering, and other techniques.
CRFsuite
CRFSuite is an implementation of Conditional Random Fields (CRFs) [Lafferty01, Sha03] for labeling sequential data. Among the various implementations of CRFs, this software provides following features.
Stephen Marsland
The book is aimed at computer science and engineering undergraduates studing machine learning and artificial intelligence.
There are lots of Python code examples in the book, and the code is available here. Where special datasets are used they are provided with the code, and there are links to additional datasets at the bottom of the page.
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The book is aimed at computer science and engineering undergraduates studing machine learning and artificial intelligence.
There are lots of Python code examples in the book, and the code is available here. Where special datasets are used they are provided with the code, and there are links to additional datasets at the bottom of the page.
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