I've been reading through some stuff related to Restricted Boltzmann Machines (RBM). By wikipedia for example I can see the graphical model is in general very similar to a standard Feed Forward Network (FFN), but on the other hand there's the training which is based on Gibbs Sampling.
What I don't get though is the difference, if there's any, between RBM and FFN, apart from the training process. The basic algorithm to train a ANN is gradient descent, but you can use in general other optimization methods, such as L-BFGS for example.
So if the difference is only the training method it doesn't seem to me they're different networks.
On the other hand some people when introduce RBM starts with probabilistic graphical models, which makes me think maybe an RBM is different from a FFN.
Can you clarify what exactly is an RBM then?