Artificial neural network

Artificial neural network (ANN) is a computational model inspired by the human brain's own method of processing information. It is a key component of artificial intelligence and is used in machine learning, where it processes complex data inputs into a format that computers can understand.

Overview

An artificial neural network is made up of a collection of connected nodes or "neurons" which are designed to simulate the way the human brain works. These networks are capable of learning from experience, improving their performance as they are exposed to more and more data.

Structure

The structure of an artificial neural network is composed of three types of layers: the input layer, the hidden layer, and the output layer. Each layer is made up of nodes, or "neurons", which are connected to each other through "synapses". The strength of these connections, or "weights", can be adjusted during the learning process.

Learning Process

The learning process in an artificial neural network involves adjusting the weights of the connections between the neurons. This is done through a process called backpropagation, which involves feeding data into the network, comparing the network's output to the desired output, and then adjusting the weights to reduce the difference.

Applications

Artificial neural networks have a wide range of applications in various fields. They are used in machine learning and deep learning algorithms to process complex data inputs. They are also used in pattern recognition, data mining, and natural language processing.

See Also

• Deep learning
• Machine learning
• Artificial intelligence
• Backpropagation
• Pattern recognition

References

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