# deep belief network

> type of artificial neural network

**Wikidata**: [Q16954980](https://www.wikidata.org/wiki/Q16954980)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Deep_belief_network)  
**Source**: https://4ort.xyz/entity/deep-belief-network

## Summary
A deep belief network is a type of artificial neural network, which is a computational model used in machine learning. It is based on connected, hierarchical functions and is a subclass of Bayesian networks and artificial neural networks. Deep belief networks are used in various applications, including machine learning and artificial intelligence.

## Key Facts
- A deep belief network is a type of artificial neural network.
- It is a subclass of Bayesian network and artificial neural network.
- The wikipedia title for deep belief network is "Deep belief network".
- Deep belief networks have aliases such as DBN, 深層信念ネットワーク, 深度置信网络, and 深度信念網絡.
- The concept of deep belief networks is described in Scholarpedia under the article ID "Deep_belief_networks".
- Deep belief networks are related to artificial neural networks, which have a sitelink count of 79.
- The wikipedia page for deep belief network is available in 9 languages, including ar, ca, en, es, fa, ko, ru, uk, and zh_yue.

## FAQs
### Q: What is a deep belief network?
A: A deep belief network is a type of artificial neural network, which is a computational model used in machine learning. It is based on connected, hierarchical functions and is a subclass of Bayesian networks and artificial neural networks. Deep belief networks are used in various applications, including machine learning and artificial intelligence.

### Q: What are the aliases for deep belief networks?
A: The aliases for deep belief networks include DBN, 深層信念ネットワーク, 深度置信网络, and 深度信念網絡.

### Q: Where can I find more information about deep belief networks?
A: More information about deep belief networks can be found on Wikipedia, Scholarpedia, and other online resources, including the article "Deep_belief_networks" on Scholarpedia.

## Why It Matters
Deep belief networks are significant in the field of artificial intelligence and machine learning because they provide a powerful tool for modeling complex data. They are capable of learning and representing intricate patterns in data, making them useful for applications such as image recognition, speech recognition, and natural language processing. The development of deep belief networks has enabled researchers to build more sophisticated artificial neural networks, which has led to significant advancements in the field of artificial intelligence.

## Notable For
- Being a type of artificial neural network that is a subclass of Bayesian networks.
- Having a range of aliases, including DBN, 深層信念ネットワーク, 深度置信网络, and 深度信念網絡.
- Being described in Scholarpedia under the article ID "Deep_belief_networks".

## Body
### Introduction to Deep Belief Networks
Deep belief networks are a type of artificial neural network that is based on connected, hierarchical functions. They are a subclass of Bayesian networks and artificial neural networks.

### Properties of Deep Belief Networks
- Deep belief networks have a range of aliases, including DBN, 深層信念ネットワーク, 深度置信网络, and 深度信念網絡.
- They are described in Scholarpedia under the article ID "Deep_belief_networks".
- The wikipedia page for deep belief network is available in 9 languages, including ar, ca, en, es, fa, ko, ru, uk, and zh_yue.

### Related Concepts
- Artificial neural networks: Deep belief networks are a type of artificial neural network, which is a computational model used in machine learning.
- Bayesian networks: Deep belief networks are a subclass of Bayesian networks, which are a type of probabilistic graphical model.

## References

1. [OpenAlex](https://docs.openalex.org/download-snapshot/snapshot-data-format)