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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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The network, except the last layer, is split into two copies, each run on one GPU. [1] The entire structure can be written as (CNN → RN → MP)² → (CNN³ → MP) → (FC → DO)² → Linear → softmax. where CNN = convolutional layer (with ReLU activation) RN = local response normalization; MP = maxpooling
Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. [1] The GRU is like a long short-term memory (LSTM) with a gating mechanism to input or forget certain features, [2] but lacks a context vector or output gate, resulting in fewer parameters than LSTM. [3]
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 ...
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 ( talk ) 20:51, 6 March 2019 (UTC) [ reply ]
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 .