37 skills found · Page 1 of 2
alexarnimueller / SomSelf organizing Kohonen map in Python with periodic boundary conditions
FlorentF9 / DESOM:globe_with_meridians: Deep Embedded Self-Organizing Map: Joint Representation Learning and Self-Organization
abhinavralhan / Kohonen MapsImplementation of SOM and GSOM
spiglerg / Kohonen SOM TensorflowTensorflow implementation of the Kohonen Self Organizing Map
diego-vicente / Ntnu SomUsing Self-Organizing Maps for Travelling Salesman Problem
EklavyaFCB / EMNIST Kohonen SOMUsing Kohonen's algorithm to make a Self Organising Map using the EMNIST database on handwritten alphabets.
LiScI-Lab / SOM.jlKohonen's self-organising maps for Julia
HITS-AIN / PINKParallelized rotation and flipping INvariant Kohonen maps
jlauron / KohonenKohonen Self Organizing Maps algorithm implementation in python, with other machine learning algorithms for comparison (kmeans, knn, svm, etc)
mljs / Somself-organizing map (SOM) / Kohonen network
shanealynn / Kohonen Self Organising Maps In RUsing Kohonen self organising maps in R for customer segmentation and analysis.
FlorentF9 / Sparkml Som:sparkles: Spark ML implementation of SOM algorithm (Kohonen self-organizing map)
sumanth-bmsce / Unsupervised Extreme Learning MachineUnsupervised Extreme Learning Machine(ELM) is a non-iterative algorithm used for feature extraction. This method is applied on the IRIS Dataset for non-linear feature extraction and clustering using k-means, Self Organizing Maps(Kohonen Network) and EM Algorithm
dashaub / Kohonen4jKohonen Self-Organizing Maps in Java
seracio / KohonenA basic implementation of a Kohonen map in JavaScript
IDSIA / Kohonen VaeOfficial repository for the paper "Topological Neural Discrete Representation Learning à la Kohonen" (ICML 2023 Workshop on Sampling and Optimization in Discrete Space)
albertnadal / KohonenNeuralNetworkC implementation of the Kohonen Neural Network (SOM algorithm)
jcfaracco / Xpysom DaskXPySom-dask is a minimalistic implementation of batch Self Organizing Map algorithm using Dask. This is a mirror of the original XPySom.
HRakesh / Healthcare Data Analysis On PIMA Indian Diabetes DatabaseKnowledge Discovery in Database. * In this project we focused our analysis on applying data analysis techniques, create visualizations and interpret the models using histograms, scatter plots and many other visual plots etc. to uncover the reason for high diabetic outcome among the PIMA Indian women. * Predict whether the patient is diagnosed with diabetes based on diagnostic measurements available in the dataset. * We obtained this dataset from Kaggle and built some supervised explanatory models (classification tree and logistic regression) and predictive models (KNN); also tried Unsupervised techniques such as K-means Clustering and Kohonen's Self-Organizing Map (SOM).
tonegas / PyNetComputation Graph framework implemented using only NumPy
