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  2. Backpropagation - Wikipedia

    en.wikipedia.org/wiki/Backpropagation

    In machine learning, backpropagation is a ... "6.5 Back-Propagation and Other Differentiation Algorithms". Deep Learning. ... Backpropagation neural network tutorial ...

  3. Backpropagation through time - Wikipedia

    en.wikipedia.org/wiki/Backpropagation_through_time

    Back_Propagation_Through_Time(a, y) // a[t] is the input at time t. y[t] is the output Unfold the network to contain k instances of f do until stopping criterion is met: x := the zero-magnitude vector // x is the current context for t from 0 to n − k do // t is time. n is the length of the training sequence Set the network inputs to x, a[t ...

  4. Rprop - Wikipedia

    en.wikipedia.org/wiki/Rprop

    Rprop, short for resilient backpropagation, is a learning heuristic for supervised learning in feedforward artificial neural networks. This is a first-order optimization algorithm. This algorithm was created by Martin Riedmiller and Heinrich Braun in 1992. [1]

  5. Deep backward stochastic differential equation method

    en.wikipedia.org/wiki/Deep_backward_stochastic...

    Introduction to Deep Learning. Deep Learning is a machine learning method based on multilayer neural networks. Its core concept can be traced back to the neural computing models of the 1940s. In the 1980s, the proposal of the backpropagation algorithm made the training of multilayer neural

  6. Mathematics of artificial neural networks - Wikipedia

    en.wikipedia.org/wiki/Mathematics_of_artificial...

    Backpropagation training algorithms fall into three categories: steepest descent (with variable learning rate and momentum, resilient backpropagation); quasi-Newton (Broyden–Fletcher–Goldfarb–Shanno, one step secant);

  7. Delta rule - Wikipedia

    en.wikipedia.org/wiki/Delta_rule

    is a small constant called learning rate g ( x ) {\\displaystyle g(x)} is the neuron's activation function g ′ {\\displaystyle g'} is the derivative of g {\\displaystyle g}

  8. Almeida–Pineda recurrent backpropagation - Wikipedia

    en.wikipedia.org/wiki/Almeida–Pineda_recurrent...

    Almeida–Pineda recurrent backpropagation is an extension to the backpropagation algorithm that is applicable to recurrent neural networks. It is a type of supervised learning . It was described somewhat cryptically in Richard Feynman 's senior thesis, and rediscovered independently in the context of artificial neural networks by both Fernando ...

  9. Multilayer perceptron - Wikipedia

    en.wikipedia.org/wiki/Multilayer_perceptron

    However, it was not the backpropagation algorithm, and he did not have a general method for training multiple layers. In 1965, Alexey Grigorevich Ivakhnenko and Valentin Lapa published Group Method of Data Handling. It was one of the first deep learning methods, used to train an eight-layer neural net in 1971. [14] [15] [16]