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angular-daterange

angular-gplaces

angular-retina

Add support for Retina displays when using element attribute "ng-src"

angular-strap

Bootstrap directives for Angular

dependencies_flutter.dart

A simple and modular dependency injection system for Flutter.

exercises

findgenepattern

flutter

Flutter makes it easy and fast to build beautiful mobile apps.

flutter_svg

SVG parsing, rendering, and widget library for Flutter

google-url

Google's URL parsing library

hashfill

Fill geofences with geohashes

hive-json-serde

Read - Write JSON SerDe for Apache Hive.

hunt

A C++ implementation of the HunT application

iugu-java

jdeferred

Java Deferred/Promise library similar to JQuery.

jml

Jeremy's Machine Learning Library

kermit

A full implementation of kermit protocol over UDP.

killbill-client-java

Java client library for Kill Bill

leveldb

Mirror of Google's leveldb project

lockfree

C++ Lock free structure Implementations

lovefield

Lovefield is a relational database for web apps. Written in JavaScript, works cross-browser. Provides SQL-like APIs that are fast, safe, and easy to use.

mk_parser

node-phantom

bridge to PhantomJS from Node.js

nsfp

NSFP Charite Project

puppet-rocketchat

Puppet module that install Rocket.Chat

rtbkit

RTBkit: a real-time bidder framework

soa

Repository for service oriented architecture code and components.

som

SOM - Self organizing Map is a Swing application that implements the Self organizing map algorithm. Self-organizing map (SOM) is a type of artificial neural network that is trained using unsupervised learning to produce low-dimensional representation of the training samples while preserving the topological properties of the input space. Self-Organizing Map showing US Congress voting patterns visualized in Synapse Self-Organizing Map showing US Congress voting patterns visualized in Synapse This makes SOM useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. The model was first described as an artificial neural network by the Finnish professor Teuvo Kohonen, and is sometimes called a Kohonen map. Like most artificial neural networks, SOMs operate in two modes: training and mapping. Training builds the map using input examples. It is a competitive process, also called vector quantization. Mapping automatically classifies a new input vector.

sort

Sort Algorightms in C

spyne

This is a simple, easily extendible rpc library that supports multiple protocols and provides several useful tools for creating, publishing and consuming online services in python.