Pinned Repositories
angular-daterange
angular-gplaces
angular-retina
Add support for Retina displays when using element attribute "ng-src"
angular-strap
Bootstrap directives for Angular
exercises
findgenepattern
flutter
Flutter makes it easy and fast to build beautiful mobile apps.
kermit
A full implementation of kermit protocol over UDP.
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.
tinyhttp
An implementation of a HTTP Server Framework written in java. I started this project, because I couldn't find a Tiny HTTP Server framework suitable for my necessities, such as Session, Cookies, HTTP/HTTPS, Asynchronous IO, etc.
leobispo's Repositories
leobispo/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.
leobispo/kermit
A full implementation of kermit protocol over UDP.
leobispo/findgenepattern
leobispo/lockfree
C++ Lock free structure Implementations
leobispo/nsfp
NSFP Charite Project
leobispo/sort
Sort Algorightms in C
leobispo/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.
leobispo/Stock