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In machine learning, backpropagation (backprop, BP) is a widely used algorithm for training feedforward neural networks. Generalizations of backpropagation exist for other artificial neural networks (ANNs), and for functions generally. These classes of algorithms are all referred to generically as "backpropagation". In fitting a neural network, backpropagation computes the gradient of the loss function with respect to the weights of the network for a single input–output example, and does so efficiently, unlike a naive direct computation of the gradient with respect to each weight individually....
Stochastic neural networks are a type of artificial neural networks built by introducing random variations into the network, either by giving the network's neurons stochastic transfer functions, or by giving them stochastic weights. This makes them useful tools for optimization problems, since the random fluctuations help it escape from local minima.
An example of a neural network using stochastic transfer functions is a Boltzmann machine. Each neuron is binary valued, and the chance of it firing depends on the other neurons in the network.
Stochastic neural networks have found applications in...
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Looking at the tags, I see that transformation tag is the first tag without a tag wiki. Upon further investigation I see questions that are barely related. Some of them are geometry related, some are to do with linear algebra and others are even to do with integration. Is this tag really construc...
Proposal: Eliminate the tag transformation.
The tag transformation is too broad—there are a large number of things in mathematics called "transformations" coming from diverse parts of mathematics, and tagging a question with this tag does nothing to refine or sort these different notions. I...
