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<h2 id="___sec0" class="anchor">Recurrent neural networks: Overarching view </h2>
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on feedforward neural networks. The output or the
activations flow only in one direction, from the input layer to the
output layer.
<p>
A recurrent neural network (RNN) looks very much like a feedforward
neural network, except that it also has connections pointing
backward.
<p>
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
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<!-- navigation toc: --> <li><a href="._Recurrent-bs001.html#___sec0" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
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<h2 id="___sec1" class="anchor">Set up of an RNN </h2>
<p>
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at a given time \( t \) and outputs \( y_t \). Introducing time as a variable
offers an intutitive way of understanding these networks. In addition
to the inputs \( x_t \), the layer at a time \( t \) receives also as input
the output from the previous layer \( t-1 \), that is \( y_{t1} \).
<p>
This means also that we need to have weights that link both the inputs \( x_t \) to the outputs \( y_t \) as well as weights that link
the output from the previous time \( y_{t-1} \) and \( y_t \). The figure here shows an example of a simple RNN.
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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 =====
Till now our focus has been, including convolutional neural networks as well,
on feedforward neural networks. The output or 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 that it also has connections pointing
backward.
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 =====
The figure here displays a simple example of an RNN, with inputs $x_t$
at a given time $t$ and outputs $y_t$. Introducing time as a variable
offers an intutitive way of understanding these networks. In addition
to the inputs $x_t$, the layer at a time $t$ receives also as input
the output from the previous layer $t-1$, that is $y_{t1}$.
This means also that we need to have weights that link both the inputs $x_t$ to the outputs $y_t$ as well as weights that link
the output from the previous time $y_{t-1}$ and $y_t$.
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doc/src/Recurrent/Recurrent.tex.old~~
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% matching end for #ifdef PREAMBLE
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% ------------------- main content ----------------------
% ----------------- title -------------------------
\thispagestyle{empty}
\begin{center}
{\LARGE\bf
\begin{spacing}{1.25}
Data Analysis and Machine Learning: Recurrent neural networks
\end{spacing}
}
\end{center}
% ----------------- author(s) -------------------------
\begin{center}
{\bf Morten Hjorth-Jensen${}^{1, 2}$} \\ [0mm]
\end{center}
\begin{center}
% List of all institutions:
\centerline{{\small ${}^1$Department of Physics, University of Oslo}}
\centerline{{\small ${}^2$Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University}}
\end{center}
% ----------------- end author(s) -------------------------
% --- begin date ---
\begin{center}
Jan 8, 2019
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% --- end date ---
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% !split
\subsection{Recurrent neural networks: Overarching view}
Till now our focus has been, including convolutional neural networks as well,
on feedforward neural networks. The output or 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 that it also has connections pointing
backward.
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
\subsection{Set up of an RNN}
The figure here displays a simple example of an RNN, with inputs $x_t$
at a given time $t$ and outputs $y_t$. Introducing time as a variable
offers an intutitive way of understanding these networks. In addition
to the inputs $x_t$, the layer at a time $t$ receives also as input
the output from the previous layer $t-1$, that is $y_{t1}$.
This means also that we need to have weights that link both the inputs $x_t$ to the outputs $y_t$ as well as weights that link
the output from the previous time $y_{t-1}$ and $y_t$. The figure here shows an example of a simple RNN.
% ------------------- end of main content ---------------
\end{document}
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