A GPU (CUDA) based Artificial Neural Network library
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Updated
Sep 25, 2021 - C++
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.
Implementation of self-organizing map using OpenMP and CUDA
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