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A perceptron traditionally used a Heaviside step function as its nonlinear activation function. However, the backpropagation algorithm requires that modern MLPs use continuous activation functions such as sigmoid or ReLU. [8] Multilayer perceptrons form the basis of deep learning, [9] and are applicable across a vast set of diverse domains. [10]
It uses a deep multilayer perceptron with eight layers. [6] It is a supervised learning network that grows layer by layer, where each layer is trained by regression analysis . Useless items are detected using a validation set , and pruned through regularization .
In 1961, Frank Rosenblatt described a three-layer multilayer perceptron (MLP) model with skip connections. [16]: 313, Chapter 15 The model was referred to as a "cross-coupled system", and the skip connections were forms of cross-coupled connections. During the late 1980s, "skip-layer" connections were sometimes used in neural networks.
The perceptron algorithm is also termed the single-layer perceptron, to distinguish it from a multilayer perceptron, which is a misnomer for a more complicated neural network. As a linear classifier, the single-layer perceptron is the simplest feedforward neural network .
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A multilayer perceptron (MLP) is a misnomer for a modern feedforward artificial neural network, consisting of fully connected neurons (hence the synonym sometimes used of fully connected network (FCN)), often with a nonlinear kind of activation function, organized in at least three layers, notable for being able to distinguish data that is not ...
In quantum neural networks programmed on gate-model quantum computers, based on quantum perceptrons instead of variational quantum circuits, the non-linearity of the activation function can be implemented with no need of measuring the output of each perceptron at each layer.
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