The term Perceptron is at home in the field of artificial intelligence and automation.
A perceptron is the simplest model of an artificial nerve cell. It was developed to teach computers how to learn - similar to the way our brain processes information. Think of the perceptron as a filter: It takes in different pieces of information and decides how to categorise them based on simple rules.
For example, a perceptron receives numbers that describe certain characteristics - such as the shape and size of objects in photos. It weighs up these features and then makes a decision, for example: "There is an apple in this picture" or "not an apple". If the perceptron makes mistakes, it learns from these errors and adapts its rules. This enables it to make better and better decisions over time.
Today, perceptrons are the building blocks of modern artificial intelligence systems, such as those used for speech recognition or quality control in production. The basic idea - simple learning by trial and error - has laid the foundation for today's intelligent computer systems.
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