What does each RBM achieve?
	• It divides the task of modeling the data into two tasks and leaves the second task to the next RBM – Task 1: Learn generative weights that can convert the posterior distribution over the hidden units into the data. – Task 2: Learn to model the posterior distribution over the hidden units that is produced by applying the transpose of the generative weights to the data • Task 2 is guaranteed to be easier (for the next RBM) than modeling the original data.
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