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The gradient thus does not vanish in arbitrarily deep networks. Feedforward networks with residual connections can be regarded as an ensemble of relatively shallow nets. In this perspective, they resolve the vanishing gradient problem by being equivalent to ensembles of many shallow networks, for which there is no vanishing gradient problem. [17]
Long short-term memory (LSTM) [1] is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem [2] commonly encountered by traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models , and other sequence learning methods.
Backpropagation computes the gradient of a loss function with respect to the weights of the network for a single input–output example, and does so efficiently, computing the gradient one layer at a time, iterating backward from the last layer to avoid redundant calculations of intermediate terms in the chain rule; this can be derived through ...
A residual block in a deep residual network. Here, the residual connection skips two layers. 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.
Packet Tracer is a cross-platform visual simulation tool designed by Cisco Systems that allows users to create network topologies and imitate modern computer networks. The software allows users to simulate the configuration of Cisco routers and switches using a simulated command line interface.
NPF is designed for high performance on SMP systems and for easy extensibility. It supports various forms of Network Address Translation (NAT), stateful packet inspection, tree and hash tables for IP sets, bytecode (BPF or n-code) for custom filter rules and other features.
In a neural network, batch normalization is achieved through a normalization step that fixes the means and variances of each layer's inputs. Ideally, the normalization would be conducted over the entire training set, but to use this step jointly with stochastic optimization methods, it is impractical to use the global information.
I really don't see the point of having both vanishing gradient and exploding gradient pages. We just have two inbound redirects, and bold both inbound terms in the lead. Should be fine IMO. — MaxEnt 00:10, 21 May 2017 (UTC) It is a well known problem in ML and pretty much everyone calls it the vanishing gradient problem.