A GPU (CUDA) based Artificial Neural Network library

Efficient Self-Organizing Map for Sparse Data

Autonomous Dynamic Learning Apprentice System

🧠 💡 📈 A project based in High Performance Computing. This project was built using CUDA (Compute Unified Device Architecture), C++ (C Plus Plus), C, CMake and JetBrains CLion. The scenario of the project was a GPU-based implementation of the Self-Organising-Maps (S.O.M.) algorithm for Artificial Neural Networks (A.N.N.), with the support of CUDA …

Neural network with learning without a teacher, performing the task of visualization and clustering.

Self Organizing Map (SOM) is a type of Artificial Neural Network (ANN) that is trained using an unsupervised, competitive learning to produce a low dimensional, discretized representation (feature map) of higher dimensional data.

A self-organizing feature map which is trained on a random three-dimensional (RGB) input space.

GPUMLib Submodule

Parallelization of the self-organizing map (Kohhonen )

C++/Qt tool to turn images into mosaics using set of icons.

SOM analysis with R

A very basic C++ code for developing a self-organizing map (SOM) for data clustering (No GUI Visualization)

EdgeSOM: Distributed Hierarchical Edge-driven IoT Data Analytics Framework

Travelling Salesman Problem (TSP) solver, two approaches with visualization.

Self-organizing maps implementation.

Old implementation of an SOM using MFC

C++での自己組織化マップ

Implementation of self-organizing map using OpenMP and CUDA