The perceptron is the simplest artificial neuron: it takes several inputs, multiplies them by its weights, adds a bias and applies an activation function that decides between two outputs. Frank Rosenblatt introduced it in 1958, and it remains the basic building block of every modern neural network.
A fully connected neural network, also called a dense network, is the fundamental architecture of deep learning: every neuron in a layer connects to all neurons in the previous and next layer. This total connectivity lets it approximate any continuous function, though its computational cost grows quadratically with the number of neurons.
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