# deep neural network

> artificial neural network with more than one hidden layer

**Wikidata**: [Q51289900](https://www.wikidata.org/wiki/Q51289900)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Deep_neural_network)  
**Source**: https://4ort.xyz/entity/deep-neural-network

## Summary
A deep neural network is an artificial neural network with more than one hidden layer. This architecture enables it to learn complex patterns through hierarchical feature representation.

## Key Facts
- Is a subclass of artificial neural network.
- Defined by having more than one hidden layer between input and output layers.
- The multiple hidden layers allow for the learning of hierarchical features.
- Can model highly complex, non-linear relationships in data.
- Forms the foundation for many state-of-the-art machine learning models.
- Enables automatic feature extraction without manual engineering.

## FAQs
### Q: What makes a neural network 'deep'?
A: Having more than one hidden layer distinguishes deep neural networks from shallow networks. This depth allows for the representation of increasingly abstract features.

### Q: Why are multiple hidden layers important?
A: Multiple layers enable the network to learn hierarchical representations, where lower layers detect simple features and higher layers combine them into complex concepts.

### Q: What problem does depth solve?
A: Depth allows the network to model intricate patterns and relationships in data that would be impossible for shallow networks to capture effectively.

## Why It Matters
Deep neural networks solve the problem of learning complex representations directly from raw data. Their hierarchical learning capability enables breakthroughs in fields like computer vision, natural language processing, and speech recognition. By automating feature extraction, they reduce the need for manual data preprocessing and allow systems to achieve human-level performance on tasks like image classification and machine translation. This capability has revolutionized artificial intelligence applications, making them more powerful and accessible.

## Notable For
- Defining characteristic: Possessing more than one hidden layer.
- Hierarchical learning: Enabling the representation of features at multiple levels of abstraction.
- Foundation for modern AI: Underpinning state-of-the-art models in diverse domains.
- Subclass relationship: Explicitly categorized under artificial neural networks.

## Body
### Definition
A deep neural network is a specific type of artificial neural network characterized by its architecture containing more than one hidden layer. This structural definition distinguishes it from shallow neural networks, which have only one hidden layer.

### Architecture
The core architecture consists of an input layer, multiple hidden layers, and an output layer. Each layer is composed of artificial neurons connected in a feedforward manner. The presence of multiple hidden layers is the defining feature.

### Hierarchical Learning
The multiple hidden layers facilitate hierarchical feature learning. Lower layers typically learn to detect simple patterns or features in the input data. Higher layers progressively combine these simple features into more complex and abstract representations. This layered approach allows the network to model intricate, non-linear relationships within the data.

### Applications
This architecture is fundamental to modern machine learning systems. It powers applications in image recognition, natural language understanding, speech synthesis, and many other domains where complex pattern recognition is required.

## Schema Markup
```json
{
  "@context": "https://schema.org",
  "@type": "Thing",
  "name": "deep neural network",
  "description": "An artificial neural network with more than one hidden layer.",
  "sameAs": [
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    "https://en.wikipedia.org/wiki/Deep_learning"
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## References

1. [Source](https://www.youtube.com/watch?v=wQ8BIBpya2k)