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A residual neural network (also referred to as a residual network or ResNet) [1] is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs. It was developed in 2015 for image recognition , and won the ImageNet Large Scale Visual Recognition Challenge ( ILSVRC ) of that year.
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This category is for particular subtypes of neural network, such as Recurrent neural network, or Convolutional neural network.Specific models (which have been trained to a particular purpose) or software implementations should not be placed in this category, but instead in Category:Neural network software or one of its descendants.
The Ford–Fulkerson method or Ford–Fulkerson algorithm (FFA) is a greedy algorithm that computes the maximum flow in a flow network.It is sometimes called a "method" instead of an "algorithm" as the approach to finding augmenting paths in a residual graph is not fully specified [1] or it is specified in several implementations with different running times. [2]
I have no idea why DenseNets are linked to Sparse network. DenseNets is a moinker used for a specific way to implement residual neural networks. If the link text had been "dense networks" it could have made sense to link to an opposite. Jeblad 20:51, 6 March 2019 (UTC)
Start downloading a Wikipedia database dump file such as an English Wikipedia dump. It is best to use a download manager such as GetRight so you can resume downloading the file even if your computer crashes or is shut down during the download. Download XAMPPLITE from (you must get the 1.5.0 version for it to work). Make sure to pick the file ...
In machine learning, the Highway Network was the first working very deep feedforward neural network with hundreds of layers, much deeper than previous neural networks. [ 1 ] [ 2 ] [ 3 ] It uses skip connections modulated by learned gating mechanisms to regulate information flow, inspired by long short-term memory (LSTM) recurrent neural networks .