Fine-tuning with a contrastive version of the “wake-sleep” algorithm
		After learning many layers of features, we can fine-tune the features to improve generation. 1.  Do a stochastic bottom-up pass – Adjust the top-down weights to be good at reconstructing the feature activities in the layer below. 2. Do a few iterations of sampling in the top level RBM -- Adjust the weights in the top-level RBM. 3. Do a stochastic top-down pass – Adjust the bottom-up weights to be good at reconstructing the feature activities in the layer above.