This is an implementation of a simple linear model to classify handwritten digits from the MNIST dataset using C and Cuda. It doesn't use any libraries such as PyTorch or Tensorflow, and it was not designed to be extensible. This was really just practice for me to learn how to use CUDA, and learn about backpropagation & gradient descent at a low level. This implementation is also not memory optimized. It uses pitched memory allocations for the CUDA kernels as this is supposedly faster than just using non-strided arrays, but other than that there are no speed optimizations.
To compile, simply run make install. Note, to compile and run this program, you need to have CUDA, NVidia drivers, and an NVidia GPU.