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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

Architectural style transfer using deep-learning. Implemented for Computer Vision 2016 (Princeton University) by Richard Du, Yash Patel, and Jason Shi

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