Golden
Deep belief network

Deep belief network

In machine learning, a deep belief network (DBN) is a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer.

In machine learning, a deep belief network (DBN) is a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. When trained on a set of examples without supervision, a DBN can learn to probabilistically reconstruct its inputs. After this learning step, a DBN can be further trained with supervision to perform classification.



DBNs can be viewed as a composition of simple, unsupervised networks such as restricted Boltzmann machines (RBMs) or autoencoders, where each sub-network's hidden layer serves as the visible layer for the next. An RBM is an undirected, generative energy-based model with a "visible" input layer and a hidden layer and connections between but not within layers. This composition leads to a fast, layer-by-layer unsupervised training procedure, where contrastive divergence is applied to each sub-network in turn, starting from the "lowest" pair of layers (the lowest visible layer is a training set). The observation that DBNs can be trained greedily, one layer at a time, led to one of the first effective deep learning algorithms. Overall, there are many attractive implementations and uses of DBNs in real-life applications and scenarios (e.g., electroencephalography, drug discovery).

Timeline

People

Name
Role
LinkedIn







Further reading

Title
Author
Link
Type
Date

Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection

JT Turner, Adam Page, Tinoosh Mohsenin, Tim Oates

Academic paper



DeepQA: Improving the estimation of single protein model quality with deep belief networks

Renzhi Cao, Debswapna Bhattacharya, Jie Hou, Jianlin Cheng

Academic paper



Documentaries, videos and podcasts

Title
Date
Link





Companies

Company
CEO
Location
Products/Services









References