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Hands-On Machine Learning with C# by Matt R. Cole

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Layering

Once our RBM learns the structure of the input data, which is related to the activations made in our first hidden layer, the data gets passed down to the next hidden layer. The first hidden layer then becomes the new visible layer. The activations we created in the hidden layer now become our inputs. They will be multiplied by the weights in the new hidden layer to produce another set of activations. This process continues through all the hidden layers in our network. The hidden layer becomes the visible layer, we have another hidden layer whose weights we will use, and we repeat. Each new hidden layer results in adjusted weights, until we get to the point where we can recognize the input from the previous layer.

To elaborate just ...

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