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What is a Deep Belief Network ?
A deep belief network (DBN) is a generative graphical model, or a type of deep neural network, constructed of multiple layers of hidden units with connections across the levels but not between units within each layer in machine learning.
A DBN may learn to probabilistically recreate its inputs when trained on a set of examples without supervision. After that, the layers serve as feature detectors. A DBN can be further taught under supervision to do categorization after completing this learning stage.
Deep Belief Network DBNs are unsupervised networks such as Restricted Boltzmann Machines (RBMs) or autoencoders. Where the hidden layer of each sub-network serves as the visible layer for the next. An RBM is a generative energy-based undirected model with a "visible" input layer and a hidden layer and connections between but not within layers. This results in a quick, unsupervised layer-by-layer training approach, in which contrastive divergence is applied to each subnetwork in turn, starting with the "lowest" pair of layers (the lowest visible layer is a training set).
One of the earliest effective deep learning algorithms in observations. Deep Belief Network DBNs may be trained greedily, one layer at a time.
How to train mode using Deep Belief Network DBNs ?
The first step is to train a property layer that can directly obtain input signals from pixels. In a second hidden layer, learn the features of the previously obtained features by treating the values of this layer as pixels. The lower bound on the log likelihood of the training data set improves every time additional layer of properties or features is added to the belief network.
How does it work ?
The Greedy learning algorithm is used to pre-train DBN. For learning the top-down, generative weights, the greedy learning method employs a layer-by-layer approach. On the top two hidden layers, we run numerous steps of Gibbs sampling in DBN. The top two hidden layers define the RBM, thus this stage is effectively extracting a sample from it. Then draw a sample from the visible units. A single bottom-up pass can infer the values of the latent variables in each layer. In the bottom layer, greedy pre-training begins with an observed data vector. It then fine-tunes the generative weights in the opposite way.
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