Another explanation of the contrastive divergence learning procedure
	• Think of an RBM as an infinite sigmoid belief net with tied weights. • If we start at the data, alternating Gibbs sampling computes samples from the posterior distribution in each hidden layer of the infinite net. • In deeper layers the derivatives w.r.t. the weights are very small. – Contrastive divergence just ignores these small derivatives in the deeper layers of the infinite net. – Its silly to compute the derivatives exactly when you know the weights are going to change a lot.