quickprop algorithm neural network ppt

From Wikipedia, the free encyclopedia, jump to: navigation, search, quickprop is an iterative method for determining the minimum of the loss function of an artificial neural network, following an algorithm inspired by the.
The k-th approximation step is given by: ( k ) w i j ( k 1 ) w i j ( i j E ( k ) i j E ( k 1 ) i j E ( k ) ) displaystyle Delta (k w_ijDelta.
Sometimes, the algorithm is classified to the group of the second order learning methods.
It follows a quadratic approximation of the previous gradient step and the current gradient, which is expected to be closed to the minimum of the loss function, under the assumption that the loss function is locally approximately square, trying to describe it by means.Being w i j displaystyle w_ij the neuron j weight of its i input and E is the loss function.The procedure requires only local information of the artificial neuron to which it is applied.Retrieved from " p?titleQuickprop oldid ".The minimum is sought in the vertex of the parabola.The Quickprop algorithm is an implementation of the error backpropagation algorithm, but the network can behave chaotically during the learning phase due to large step sizes.Quickprop is an iterative method for determining the minimum of the loss function of an artificial neural network, following an algorithm inspired by the Newton.Some historical notes; Multilayer neural networks ; Backpropagation; Error.Hoff (1960 least-mean square algorithm (LMS) delta rule: three-layer networks.'Unshakeable' has been more than a year in the making, beginning in January 2013 when vatw gave several hundred schools and youth choirs free online access to MP3 recordings of the different choral parts and then watched the buzz grow as more and more schools.
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