Fine-tuning for discrimination
	• First learn one layer at a time greedily. • Then treat this as “pre-training” that finds a good initial set of weights which can be fine-tuned by a local search procedure. – Contrastive wake-sleep is one way of fine- tuning the model to be better at generation. • Backpropagation can be used to fine-tune the model for better discrimination. – This overcomes many of the limitations of standard backpropagation.