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A perceptron is a fundamental building block of artificial neural networks and is considered one of the simplest types of artificial neurons. It was introduced by Frank Rosenblatt in 1958 as a computational model inspired by the functioning of a biological neuron. The perceptron is used primarily for supervised learning tasks, especially for binary classification problems, where the goal is to categorize input data into one of two possible classes. Structurally, a perceptron consists of several key components: input nodes, weights, a bias, a summation function, and an activation function. Each input node receives a signal, which can be represented as numerical values corresponding to features of the dataset. Each input is assigned a weight, which determines the importance or contribution of that input to the final output. These weights are _________ ___ _____ __________ ________ __________ ____ ___ _______.
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