<Dl> <Dd> arg ⁡ max W E (log ⁡ P (v)) (\ displaystyle \ arg \ max _ (W) \ mathbb (E) \ left (\ log P (v) \ right)) </Dd> </Dl> <Dd> arg ⁡ max W E (log ⁡ P (v)) (\ displaystyle \ arg \ max _ (W) \ mathbb (E) \ left (\ log P (v) \ right)) </Dd> <P> The algorithm most often used to train RBMs, that is, to optimize the weight vector W (\ displaystyle W), is the contrastive divergence (CD) algorithm due to Hinton, originally developed to train PoE (product of experts) models . The algorithm performs Gibbs sampling and is used inside a gradient descent procedure (similar to the way backpropagation is used inside such a procedure when training feedforward neural nets) to compute weight update . </P> <P> The basic, single - step contrastive divergence (CD - 1) procedure for a single sample can be summarized as follows: </P>

Restricted boltzmann machine expect the data to be labeled