DERI Pipes TensorFlow - Google’s latest machine learning system, open sourced for everyone Posted by Jeff Dean, Senior Google Fellow, and Rajat Monga, Technical Lead Deep Learning has had a huge impact on computer science, making it possible to explore new frontiers of research and to develop amazingly useful products that millions of people use every day. Our internal deep learning infrastructure DistBelief, developed in 2011, has allowed Googlers to build ever larger neural networks and scale training to thousands of cores in our datacenters. We’ve used it to demonstrate that concepts like “cat” can be learned from unlabeled YouTube images, to improve speech recognition in the Google app by 25%, and to build image search in Google Photos. DistBelief also trained the Inception model that won Imagenet’s Large Scale Visual Recognition Challenge in 2014, and drove our experiments in automated image captioning as well as DeepDream. While DistBelief was very successful, it had some limitations.
gFacet - Visual Data Web Complex semantic querying made easy! gFacet is a new approach to explore RDF data by combining graph-based visualization with faceted filtering techniques. The facets are represented as nodes in a graph visualization and can be interactively added and removed by the users in order to produce individual search interfaces. Even multiple and distantly connected facets can be integrated in the graph facilitating the access of information from different user-defined perspectives. gFacet is based on the open source framework Adobe Flex and uses SPARQL queries to access RDF datasets. gFacet is readily configured to access data from the LOD cloud and only requires a Flash Player plugin to be executed (which is usually already installed in web browsers). All tools on this website are research prototypes that might contain errors. Read more about gFacet and the idea of graph-based faceted exploration in the following publications: Facet Graphs: Complex Semantic Querying Made Easy.
Simply Statistics | Simply Statistics RelFinder - Visual Data Web Are you interested in how things are related with each other? The RelFinder helps to get an overview: It extracts and visualizes relationships between given objects in RDF data and makes these relationships interactively explorable. Highlighting and filtering features support visual analysis both on a global and detailed level. The RelFinder is based on the open source framework Adobe Flex, easy-to-use and works with any RDF dataset that provides standardized SPARQL access. Check out the following links for some examples: The RelFinder can easily be configured to work with different RDF datasets. The RelFinder can also be more deeply integrated with your project: Integrating the RelFinder See the following examples of how the RelFinder is integrated into other projects: Ontotext applies the RelFinder to enable an exploration of relationships in the biomedical domain. All tools on this website are research prototypes that might contain errors.
Scaling Market Basket Analysis with MapReduce » Loren on the Art of MATLAB In an earlier post, today's guest blogger Toshi Takeuchi gave us an introduction to Market Basket Analysis. This week, he will discuss how to scale this technique using MapReduce to deal with larger data. Contents MapReduce in MATLAB 101 R2014b was a major update to MATLAB core functionality and one of the several new exciting features to me was MapReduce. I was primarily interested in Market Basket Analysis to analyze clickstream data and I knew web usage data extracted from web server logs would be very large. MapReduce was developed to process massive datasets in a distributed parallel computation, and it is one of the key technologies that enabled Big Data analytics. MapReduce is made up of mappers and reducers. Use datastore to designate data sourcesDefine mapper and reducer functionsUse mapreduce with datastore, the mapper and reducer to process dataStore the processed data for further analysis We start by setting up datastore. Set up source datastore. Let's review the data.
RDFScape : Home Page RDFScape is a project that brings Semantic Web "features" to the popular Systems Biology software Cytoscape. It allows to query, visualize and reason on ontologies represented in OWL or RDF within Cytoscape. A full list of features is reporte in Features. Unlike other ontology-based features in Cytoscape, RDFScape doesn't consider ontologies as annotation, but as a knowledge-base that can be interpreted through standard inference processes and through custom inference rules. The result is that ontologies can be interpreted for specific analysis needs. For instance, a pathway ontology such as biopax can be easily abstracted to an interaction network. Beside this, RDFScape offers reach query capabilities on ontologies (SPARQL,RDQL,Strings, interactive browsing) and a customizable visualization features. Current version of RDFScape is 0.4.1 and offers a rich set of features to query, visualize and overlay ontologies on other Cytoscape networks. RDFScape is released as LGPL.
Real-Time Analytics In Service of Self-Healing Ecosystems @ Netflix - DZone Cloud The Cloud Zone is brought to you in partnership with Iron.io. Discover how Microservices have transformed the way developers are building and deploying applications in the era of modern cloud infrastructure from Iron.io. Netflix's infrastructure is just too big for human eyes to check everything. That's why they built this incredible operationally intelligent system that monitors itself and basically heals itself. Video Description: "Netflix strives to provide an amazing experience to each member. The Cloud Zone is brought to you in partnership with Iron.io. Topics: netflix,cloud,aws,monitoring,operational intelligence
Piggy Bank Piggy Bank Contributing Piggy Bank is an open source software and built around the spirit of open participation and collaboration. There are several ways you can help: Blog about Piggy Bank Subscribe to our mailing lists to show your interest and give us feedback Report problems and ask for new features through our issue tracking system (but take a look at our todo list first) Send us patches or fixes to the code Publish Semantic Web data on your web site (how-to) for Piggy Bank’s consumption Write and submit new screen scrapers for others to use Research Publications on Piggy Bank: David Huynh, Stefano Mazzocchi, and David Karger. Related research: History Licensing & Legal Issues Piggy Bank is open source software and is licensed under the BSD license. Note, however, that this software ships with libraries that are not released under the same license; that we interpret their licensing terms to be compatible with ours and that we are redistributing them unmodified. Disclaimer Credits
untitled September 2009 The Plus teacher packages are designed to give teachers and students (and everyone else) easy access to Plus content on a particular subject area. Most Plus articles go far beyond the explicit maths taught at school, while still being accessible to someone doing A level maths. They put classroom maths in context by explaining the bigger picture — they explore applications in the real world, find maths in unusual places, and delve into mathematical history and philosophy. This teacher package brings together all Plus articles on graph and network theory. From bridges to networks. We have divided all our other articles into three categories: Algorithms: Many real-life problems involve finding a particular colouring of a graph or network, finding an optimal path or "flow" through a graph or network, or constructing graphs from given information. Don't forget that our sister site NRICH has many hands-on problems, activities and articles covering graph and network theory.
FUSION Semantic Registry UDDI-based Web service registries are included as a standard offering within the product suite of all major SOA vendors, serving as the foundation for establishing design-time and run-time SOA governance. Despite the success of the UDDI specification and its rapid uptake by the industry, the capabilities of its offered service discovery facilities are rather limited. The lack of machine-understandable semantics in the technical specifications and classification schemes that are used for retrieving services prevent UDDI registries from supporting fully automated and thus truly effective service discovery. The FUSION Semantic Registry is a semantically-enhanced service registry that builds on the UDDI specification and augments its service publication and discovery facilities to overcome these limitations. Kourtesis D. and Paraskakis I.
newrelic Deployment Reports Allows you to see before and after picture of your app's performance when a change has been deployed. Quickly back out of a change before it affects users in production. Transaction Tracing Transaction Tracing provides deep visibility into the cause of application performance issues down to the tiniest detail. Cross App Tracing New Relic traces transactions across tiers and services to provide end-to-end visibility and automatically maps each tier of apps to easily visualize the relationship between them. Alert Policies Manage your application policies alert channels by creating specified notification groups or leverage New Relics integrated alerting channels.