mhjensen
171 lines
5.6 KiB
HTML
171 lines
5.6 KiB
HTML
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<a class="navbar-brand" href="Recurrent-bs.html">Data Analysis and Machine Learning: Recurrent neural networks</a>
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<!-- navigation toc: --> <li><a href="#___sec0" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
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<h2 id="___sec0" class="anchor">Recurrent neural networks: Overarching view </h2>
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<p>
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Till now our focus has been, including convolutional neural networks
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as well, on feedforward neural networks. The output or the activations
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flow only in one direction, from the input layer to the output layer.
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<p>
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A recurrent neural network (RNN) looks very much like a feedforward
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neural network, except that it also has connections pointing
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backward.
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<p>
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RNNs are used to analyze time series data such as stock prices, and
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tell you when to buy or sell. In autonomous driving systems, they can
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anticipate car trajectories and help avoid accidents. More generally,
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they can work on sequences of arbitrary lengths, rather than on
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fixed-sized inputs like all the nets we have discussed so far. For
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example, they can take sentences, documents, or audio samples as
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input, making them extremely useful for natural language processing
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systems such as automatic translation and speech-to-text.
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<b>The text here is under development</b>. Planned finished mid Jan 2020.
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<li><a href="._Recurrent-bs000.html">1</a></li>
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<li><a href="._Recurrent-bs002.html">3</a></li>
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<li><a href="._Recurrent-bs003.html">4</a></li>
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<li><a href="._Recurrent-bs004.html">5</a></li>
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