GPU Machine Learning Library. This library aims to provide machine learning researchers and practitioners with a high performance library by taking advantage of the GPU enormous computational power. The library is developed in C++ and CUDA.
- Multiple Back-Propagation (MBP)
- Back-Propagation (BP)
- Radial Basis Functions (RBF)
- Non-Negative Matrix Factorization (NMF)
- Semi-Supervised Non-Negative Matrix Factorization (SSNMF)
- Restricted Boltzmann Machines (RBM)
- Deep Belief Networks (DBN)
- Autonomous Training System (ATS)
- Support Vector Machines (SVM)
- Self Organizing Maps (SOM)
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