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Sparse Som ⭐ 15

Efficient Self-Organizing Map for Sparse Data

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

Sparkml Som ⭐ 12

✨ Spark ML implementation of SOM algorithm (Kohonen self-organizing map)

Gpu Cuda Self Organising Maps ⭐ 7

🧠 💡 📈 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 (Compute Unified Device Architecture), using its offered parallel optimisations and tunings. The final goal of the project was to test the several GPU

Self Learning Algorithm ⭐ 5

Suite of self-learning algorithms.

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