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  2. Multilayer perceptron - Wikipedia

    en.wikipedia.org/wiki/Multilayer_perceptron

    If a multilayer perceptron has a linear activation function in all neurons, that is, a linear function that maps the weighted inputs to the output of each neuron, then linear algebra shows that any number of layers can be reduced to a two-layer input-output model.

  3. Bidirectional recurrent neural networks - Wikipedia

    en.wikipedia.org/wiki/Bidirectional_recurrent...

    For example, multilayer perceptron (MLPs) and time delay neural network (TDNNs) have limitations on the input data flexibility, as they require their input data to be fixed. Standard recurrent neural network (RNNs) also have restrictions as the future input information cannot be reached from the current state.

  4. Types of artificial neural networks - Wikipedia

    en.wikipedia.org/wiki/Types_of_artificial_neural...

    Each block consists of a simplified multi-layer perceptron (MLP) with a single hidden layer. The hidden layer h has logistic sigmoidal units, and the output layer has linear units. Connections between these layers are represented by weight matrix U; input-to-hidden-layer connections have weight matrix W.

  5. Multilayer perceptrons - Wikipedia

    en.wikipedia.org/?title=Multilayer_perceptrons&...

    Download QR code; Print/export Download as PDF; Printable version; In other projects Appearance. move to sidebar hide. ... Feedforward neural network#Multilayer ...

  6. Feedforward neural network - Wikipedia

    en.wikipedia.org/wiki/Feedforward_neural_network

    The bottom layer of inputs is not always considered a real neural network layer. 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 ...

  7. Activation function - Wikipedia

    en.wikipedia.org/wiki/Activation_function

    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.

  8. History of artificial neural networks - Wikipedia

    en.wikipedia.org/wiki/History_of_artificial...

    A multilayer perceptron (MLP) comprised 3 layers: an input layer, a hidden layer with randomized weights that did not learn, and an output layer. With mathematical notation, Rosenblatt described circuitry not in the basic perceptron, such as the exclusive-or circuit that could not be processed by neural networks at the time.

  9. Sigmoid function - Wikipedia

    en.wikipedia.org/wiki/Sigmoid_function

    In particular see "Chapter 4: Artificial Neural Networks" (in particular pp. 96–97) where Mitchell uses the word "logistic function" and the "sigmoid function" synonymously – this function he also calls the "squashing function" – and the sigmoid (aka logistic) function is used to compress the outputs of the "neurons" in multi-layer neural ...