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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. Torch (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Torch_(machine_learning)

    Download QR code; Print/export ... that provides read() and write() methods. nn ... What follows is an example use-case for building a multilayer perceptron using ...

  4. Probabilistic neural network - Wikipedia

    en.wikipedia.org/wiki/Probabilistic_neural_network

    A probabilistic neural network (PNN) [1] is a feedforward neural network, which is widely used in classification and pattern recognition problems.In the PNN algorithm, the parent probability distribution function (PDF) of each class is approximated by a Parzen window and a non-parametric function.

  5. 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 ...

  6. Deep learning - Wikipedia

    en.wikipedia.org/wiki/Deep_learning

    Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning.The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.

  7. Neural network (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Neural_network_(machine...

    In 1982 a recurrent neural network, with an array architecture (rather than a multilayer perceptron architecture), named Crossbar Adaptive Array [65] [66] used direct recurrent connections from the output to the supervisor (teaching ) inputs. In addition of computing actions (decisions), it computed internal state evaluations (emotions) of the ...

  8. 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.

  9. Multilayer perceptrons - Wikipedia

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

    Multilayer perceptrons. Add languages. Add links. ... Download QR code; Print/export ... Feedforward neural network#Multilayer perceptron;