Search Results for "labels"
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Showing 5 open source projects for "labels"
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
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gml4gtk
small console GML or graphviz DOT graph viewer
directed graph layout program for GML (Graph-Markup-Language) graph or partial graphviz DOT graph -
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AFNER is a C++ named entity recognition system that uses machine learning techniques. It is customisable to various domains. It also allows for multiple and overlapping named entity labels.
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Apolda is a plugin for the Gate framework (see http://sourceforge.net/projects/gate/) that annotates texts with labels of concepts from an arbitrary OWL-ontology.
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A tag based personal note taking system, written in python using sqlite. This program is designed to help you organize your thoughts. It uses tags in a way similar to Gmail's "labels" or del.icio.us tags. Any contribution welcome!
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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ExoPlanet
GUI based toolkit for running common Machine Learning algorithms.
...ExoPlanet is designed to have a minimal learning curve, allowing researchers to focus on the applicative aspect of Machine Learning rather than their implementation details. It provides algorithms for unsupervised and supervised learning, which may be done with continuous or discrete labels. Post analysis, the toolkit further automates building the visual representations for the trained model.
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