Artificial Neural Networks

   20/07/2024  Posted in Robotics

Introduction to Artificial Neural Networks

Artificial neural networks (ANN) are computing systems designed to simulate the workings of the human brain. They are composed of connected nodes, or neurons, that transmit information through the network. ANNs are used to solve complex problems in a wide range of fields, including finance, science, and engineering.

How do ANNs work?

ANNs use a process called machine learning. They are trained on a set of data and learn to recognize patterns, making predictions or decisions based on that data. ANNs are made up of several layers of interconnected neurons, with each layer processing a specific type of data. The input layer receives the data and passes it to the hidden layers, where the computation takes place. Finally, the output layer produces the result.

Applications of ANNs

ANNs have several practical applications, including image and voice recognition, fraud detection, and prediction of stock prices. They are also used in natural language processing, data analysis, and object detection. ANNs have the ability to learn from data, making them invaluable tools for businesses and researchers in various fields.

Conclusion

Artificial neural networks are complex computing systems simulating the workings of the human brain. They use machine learning to recognize patterns and make predictions or decisions based on data. ANNs have a wide range of applications and are used in finance, science, and engineering, among others. As technology advances, ANNs will continue to be a key tool in solving complex problems.
Author: Feg2