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Neuron is used to describe the fundamental processing units of these complex systems. Artificial neurons, also known as nodes, are inspired by biological neurons in the human brain. These neurons form the basis of a neural network. They receive information, perform computations on it, and pass it on, much like how neurons in the human brain transmit signals.


An artificial neuron typically receives inputs from either the original dataset or from neurons in the previous layer of the network. Each of these inputs is associated with a weight, which can be thought of as the importance or influence of that particular input to the neuron’s ultimate calculation. The artificial neuron computes a weighted sum of these inputs and then applies a function, known as an activation function, to this sum.The activation function governs the result that the neuron produces.The outputs can either be final results of the network or can be sent as inputs to other neurons in subsequent network layers.

The process of training an artificial neural network involves adjusting the weights associated with the inputs of each neuron in a way that minimizes the error between the network’s output and the expected output. This starts a feedback process, where the outcome is compared to the original intention, and the difference between them is used to adjust the weights, gradually improving the network’s performance. Despite being a simplified model of a human neuron, an artificial neuron’s functionality embodies the key aspects of its biological counterpart, enabling it to learn and adapt over time, thereby making them the cornerstone of many modern AI systems.

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