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In machine learning, backpropagation [1] is a gradient estimation method commonly used for training a neural network to compute its parameter updates. It is an efficient application of the chain rule to neural networks.
The standard method for training RNN by gradient descent is the "backpropagation through time" (BPTT) algorithm, which is a special case of the general algorithm of backpropagation. A more computationally expensive online variant is called "Real-Time Recurrent Learning" or RTRL, [ 78 ] [ 79 ] which is an instance of automatic differentiation in ...
Then, the backpropagation algorithm is used to find the gradient of the loss function with respect to all the network parameters. Consider an example of a neural network that contains a recurrent layer and a feedforward layer . There are different ways to define the training cost, but the aggregated cost is always the average of the costs of ...
Neural backpropagation is the phenomenon in which, after the action potential of a neuron creates a voltage spike down the axon (normal propagation), another impulse is generated from the soma and propagates towards the apical portions of the dendritic arbor or dendrites (from which much of the original input current originated).
A network is trained by modifying these weights through empirical risk minimization or backpropagation in order to fit some preexisting dataset. [ 5 ] Neural networks are used to solve problems in artificial intelligence , and have thereby found applications in many disciplines, including predictive modeling , adaptive control , facial ...
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);
Backpropagation; Rescorla–Wagner model – the origin of delta rule; References ... Upload file; Special pages; Permanent link; Page information; Cite this page;
Backpropagation through structure (BPTS) is a gradient-based technique for training recursive neural networks, proposed in a 1996 paper written by Christoph Goller and Andreas Küchler. [ 1 ] References