This project combines 3D real time engine with machine learning algorithms based on Gaussian Processes sparse approximations techniques with optimization via Expectation Propagation (EP). This probabilictic framework enables deep neural architectures in continous function domain. With this approach no data for prediction, like for other non-parametric models, is needed anymore. This enables batch learning and increases scaleability. The probabilistic framework is implemented for optimization on graphics card and allows generation of character animation, interaction with users or probabilistic mesh deformation. The project is developed by XXX member of XXX.
The example projects depending on a bunch of libraries, where all needed *.dlls are copied to the execution folder during build. The easiest way to create an own project: