A DBN is a multilayer belief network where each layer is an RBM stacked against one another. Apart from the first and final layers of the DBN, each layer serves as both a hidden layer to the nodes before it, and as the input layer to the nodes that come after it:
![](https://static.packt-cdn.com/products/9781788992596/graphics/assets/b27d529e-2709-4958-b65e-0f5a5829a37f.png)
Two layers in the DBN are connected by a matrix of weights. The top two layers of a DBN are undirected, which gives a symmetric connection between them, forming an associative memory. The lower two layers have directed connections from the layers above. The presence of direction converts associative memory into observed variables:
![](https://static.packt-cdn.com/products/9781788992596/graphics/assets/945bdb2d-5dbb-438d-b8fe-77902afaecb5.png)
The two most significant properties of DBNs are as follows:
- A DBN learns top-down, generative weights via an efficient, layer by layer procedure. These weights determine how the variables in one layer depend on the layer above.
- Once training is complete, the values of the...