Neural Network Defined

A neural network is a set of algorithms which is modeled loosely on the the human brain, which endeavors to recognize underlying relationships in a set of data. A neural network interprets sensory data through machine perception, labeling or clustering raw input, that are designed to recognize patterns. The concept of neural networks, which has its roots in artificial intelligence.

This type of network, works by having organized layers of highly interconnected elements or nodes, which process information using dynamic state responses to external inputs. 

There are three types of neural networks,based on the number of layers they contain. These types are:

• Feed-forward neural networks: The basic type of neural networks. The information in this network travels in a unidirectional manner, that is, only from input to processing node to output.
• Recurrent neural networks: The data flows in multiple directions in this network. 
• Convolutional neural networks: They allow encoding attributes into the input, by assuming it to be an image.

Applications of Neural Networks in a business sense include:

• Image processing
• Language processing and translation
• Route detection
• Speech recognition
• Forecasting

Advantages include:

• The ability to learn by themselves and produce the output.
• The ability to store input in its own networks instead of a database.
• The ability to learn from examples and apply them when a similar event arises.
• The ability to perform multiple tasks in parallel without affecting the system performance.

In Data Defined, we help make the complex world of data more accessible by explaining some of the most complex aspects of the field.

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