https://github.com/ggiuffre/dbnsim

A web app for training and analysing Deep Belief Networks
https://github.com/ggiuffre/dbnsim

artificial-intelligence dbn deep-belief-network neural-network rbm restricted-boltzmann-machine unsupervised-learning

Last synced: about 2 months ago
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A web app for training and analysing Deep Belief Networks

• Host: GitHub
• URL: https://github.com/ggiuffre/dbnsim
• Owner: ggiuffre
• License: mit
• Created: 2017-04-27T13:27:38.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2017-08-18T08:08:22.000Z (over 7 years ago)
• Last Synced: 2025-01-19T21:23:50.321Z (3 months ago)
• Topics: artificial-intelligence, dbn, deep-belief-network, neural-network, rbm, restricted-boltzmann-machine, unsupervised-learning
• Language: Python
• Homepage: https://ggiuffre.github.io/DBNsim/
• Size: 35.9 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# DBNsim [![Build Status](https://travis-ci.org/ggiuffre/DBNsim.svg?branch=master)](https://travis-ci.org/ggiuffre/DBNsim)

## A web app for analysing Deep Belief Networks

DBNsim is a web application for training and analysing __Deep Belief Networks__, a particular kind of artifical neural networks. DBNsim has a Python back end (with Django) and a JavaScript front end (which uses mainly Cytoscape.js and Highcharts).

Deep Belief Networks (DBNs) are a particular _architecture_ of neural nets: they are multi-layered networks where each layer is a Restricted Boltzmann Machine (RBM); in practice, a DBN is a "stack" of RBMs. An RBM is a bipartite undirected graph, typically trained with unsupervised learning.

### Documentation

If you want to know how to get, install and use the app please read the documentation, available in two formats:

* HTML ([here](https://ggiuffre.github.io/DBNsim/))

* PDF (dowload [here](https://ggiuffre.github.io/DBNsim/tex/DBNsim.pdf))