Solving the Traveling Salesman Problem using Self-Organizing Maps

🔴 MiniSom is a minimalistic implementation of the Self Organizing Maps

Python library for Self-Organizing Maps

Explore high-dimensional datasets and how your algo handles specific regions.

SuSi: Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)

🌐 Deep Embedded Self-Organizing Map: Joint Representation Learning and Self-Organization

Pytorch implementation of a Self-Organizing Map

A multi-gpu implementation of the self-organizing map in TensorFlow

Python implementation of the Epigenetic Robotic Architecture (ERA). It includes standalone classes for Self-Organizing Maps (SOM) and Hebbian Networks.

Hierarchical self-organizing maps for unsupervised pattern recognition

Pytorch implementation of Self-Organizing Map(SOM). Use MNIST dataset as a demo.

Codes and Templates from the SuperDataScience Course

Self-Organizing Map [https://en.wikipedia.org/wiki/Self-organizing_map] is a popular method to perform cluster analysis. SOM shows two main limitations: fixed map size constraints how the data is being mapped and hierarchical relationships are not easily recognizable. Thus Growing Hierarchical SOM has been designed to overcome this issues

🌐 SOMperf: Self-organizing maps performance metrics and quality indices

FlowSOM algorithm in Python, using self-organizing maps and minimum spanning tree for visualization and interpretation of cytometry data

A small Python 3 library to train Self Organizing Maps and use them to classify patterns.

A photometric redshift monstrosity

Machine Learning (ML) research within medicine and healthcare represents one of the most challenging domains for both engineers and medical specialists. One of the most desired tasks to be accomplished using ML applications is represented by disease detection. A good example of such a task is the detection of genetic abnormalities like Down synd…

Neural Network approaches for the Traveling Salesman Problem