R.-Boltzmann-machine-Py-Cpp

Implement from scratch an RBM and apply it to MINST dataset (hadwritten digit). It was implemented in Python and C++

Implement from scratch an RBM and apply it to DSET3. The RBM should be implemented fully by you (both CD-1 training and inference steps) but you are free to use library functions for the rest (e.g. image loading and management, etc.).

1. Train an RBM with a number of hidden neurons selected by you (single layer) on the MNIST data (use the training set split provided by the website).
2. Use the trained RBM to encode all the images using the corresponding activation of the hidden neurons
3. Train a simple classifier (e.g. any simple classifier in scikit) to recognize the MNIST digits using as inputs their encoding obtained at step 2. Use the standard training/test split. Show a performance metric of your choice in the presentation/handou

DSET3 (Image processing: MNIST): http://yann.lecun.com/exdb/mnist/

report.pdf