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-TITLE: Data Analysis and Machine Learning: Recurrent neural networks
-AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
-DATE: today
-
-
-!split
-===== Recurrent neural networks: Overarching view =====
-
-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.
-
-
-!split
-=====  Set up of an RNN =====
-