Fine-tuning with a contrastive divergence 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 – Use CD learning to improve the 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.