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  2. Multilayer perceptron - Wikipedia

    en.wikipedia.org/wiki/Multilayer_perceptron

    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]

  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. Neuroph - Wikipedia

    en.wikipedia.org/wiki/Neuroph

    Neuroph is an object-oriented artificial neural network framework written in Java. It can be used to create and train neural networks in Java programs. Neuroph provides Java class library as well as GUI tool easyNeurons for creating and training neural networks. It is an open-source project hosted at SourceForge under the Apache License.

  5. Types of artificial neural networks - Wikipedia

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

    [8] 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.

  6. Feedforward neural network - Wikipedia

    en.wikipedia.org/wiki/Feedforward_neural_network

    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 ...

  7. Backpropagation - Wikipedia

    en.wikipedia.org/wiki/Backpropagation

    Backpropagation computes the gradient of a loss function with respect to the weights of the network for a single input–output example, and does so efficiently, computing the gradient one layer at a time, iterating backward from the last layer to avoid redundant calculations of intermediate terms in the chain rule; this can be derived through ...

  8. Multilayer perceptrons - Wikipedia

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

    This page was last edited on 10 August 2023, at 11:09 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may ...

  9. Perceptron - Wikipedia

    en.wikipedia.org/wiki/Perceptron

    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 .