Why does greedy learning work?
		The weights, W,  in the bottom level RBM define p(v|h) and they also, indirectly, define p(h). So we can express the RBM model as
	If we leave p(v|h) alone and improve p(h), we will improve p(v). To improve p(h), we need it to be a better model of the aggregated posterior distribution over hidden vectors produced by applying W to the data.