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[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

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Nov 10, 2018

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Dec 29, 2018


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Recurrent neural networks: Overarching view

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+We have mostly looked at feedforward neural networks, where the +activations flow only in one direction, from the input layer to the +output layer. + +

+A recurrent neural network (RNN) looks very much like a feedforward +neural network, except it also has connections pointing +backward. Let’s look at the simplest possible RNN, composed of just +one neuron receiving inputs, producing an output, and sending that +output back to itself. + +

+RNNs are used to analyze time series data such as stock prices, and +tell you when to buy or sell. In autonomous driving systems, they can +anticipate car trajectories and help avoid accidents. More generally, +they can work on sequences of arbitrary lengths, rather than on +fixed-sized inputs like all the nets we have discussed so far. For +example, they can take sentences, documents, or audio samples as +input, making them extremely useful for natural language processing +systems such as automatic translation and speech-to-text. +