Solving the Traveling Salesman Problem using Self-Organizing Maps
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Updated
Dec 24, 2023 - Python
Solving the Traveling Salesman Problem using Self-Organizing Maps
🔴 MiniSom is a minimalistic implementation of the Self Organizing Maps
Codes and Templates from the SuperDataScience Course
Python library for Self-Organizing Maps
A multi-gpu implementation of the self-organizing map in TensorFlow
🌐 Deep Embedded Self-Organizing Map: Joint Representation Learning and Self-Organization
Pytorch implementation of a Self-Organizing Map
SuSi: Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)
Python implementation of the Epigenetic Robotic Architecture (ERA). It includes standalone classes for Self-Organizing Maps (SOM) and Hebbian Networks.
Pytorch implementation of Self-Organizing Map(SOM). Use MNIST dataset as a demo.
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
Hierarchical self-organizing maps for unsupervised pattern recognition
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.
Implementation of Artificial Intelligence models without using any blackbox or libraries 😎
Explore high-dimensional datasets and how your algo handles specific regions.
Python machine learning library using powerful numerical optimization methods.
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