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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.
Igel and Hüsken assigned names to them and added a new variant: [2] [3] RPROP+ is defined at A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm. [4] RPROP− is defined at Advanced Supervised Learning in Multi-layer Perceptrons – From Backpropagation to Adaptive Learning Algorithms. Backtracking is removed from ...
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 ...
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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 ...
Proposed 25% U.S. tariffs on Canada and Mexico could lead to a sharp increase in vehicle prices, auto industry executives and analysts say.
U.S. Secretary of State Marco Rubio said on Tuesday that ending Russia's war in Ukraine would be official U.S. policy under President Donald Trump, part of the administration's goal of promoting ...
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).