I am new in the field of RBMs, DBMs and I cannot understand some things. I came across the idea of feature selection using RBMs (or Deep Belief Networks). Although the Hidden nodes which make new features as a result from the processing of Visible Nodes give 0/1 as output. How do I translate the 0/1 from the Hidden Units? Which part to RBM should I take to find the best features?
In your trained RBM model, the hidden units can indeed be seen as extracted features automatically learned from the data at the visible units. However, I think most of the time you would have a hard time trying to interpret what the learned features are, but you can try by visualizing the weights for example.
In general, RBM are used to perform feature selection in order to use a classifier on the selected features without much effort for interpreting the features (scikit-learn example). And this is a great, since you do not need to handcraft the relevant features! Looking at the weights has often the role of a sanity check to track any buggy behavior.