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<meta name="Author" content="Ronan Collobert">
<meta name="Description" content="Torch Library">
<meta name="Keywords" content="Machine Learning">
<title>Torch Introduction</title>
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<center><img SRC="images/title.jpeg" ALT="[Torch]" NOSAVE height=137 width=220><img SRC="images/release.jpeg" NOSAVE height=18 width=77>
<p><img SRC="images/torche.jpeg" ALT="[XXXXX]" NOSAVE height=32 width=32></center>
<div align=right><i><font size=-1><a href="http://www.idiap.ch/~collober">Ronan
Collobert</a>,
<a href="mailto:collober@iro.umontreal.ca">collober@iro.umontreal.ca</a></font></i></div>
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<p><img SRC="images/new.jpg" ALT="[new!]" NOSAVE height=11 width=28> You
should have a look on <a href="matos/presentation.ps.gz">this presentation</a>
written by <i><a href="http://www.idiap.ch/~bengio">Samy Bengio</a></i>
which introduces main concepts in <b><i>Torch</i></b>.
<p>Here is an abstract:
<br>There are several concepts to know in <b><i>Torch</i></b>, if you plan
to use it:
<ul>
<li>
DataSet</li>
<li>
Machine</li>
<li>
Trainer</li>
<li>
Measurer</li>
</ul>
<p><br>A <i>DataSet</i> is a black-box which could provide all kind of
data. For example you have:
<ul>
<li>
StdDataSet, which manages standard datasets: examples with the same input
dimension and the same output dimension.</li>
<li>
SeqDataSet which provides a easy interface to sequence datasets used in
the Distribution package...</li>
</ul>
A <i>Machine </i>is a learning-machine. It could be:
<ul>
<li>
MLP, a multi-layered perceptron.</li>
<li>
HMM, a hidden markov model</li>
<li>
SVM, a support vector machine</li>
</ul>
A <i>Trainer</i> is a class which takes a <i>Machine</i> and a <i>DataSet</i>,
and which is able to train or test the machine. It could be:
<ul>
<li>
GMTrainer, a gradient machine trainer which can train all kind of gradient
machines. (For example MLP)</li>
<li>
EMTrainer, which trains several machines using the EM algorithm. (For example
HMM)</li>
<li>
QCTrainer, which trains all quadratic, constrained machine. (For example
SVM)</li>
</ul>
A <i>Measurer </i>is a special class which is called by a <i>Trainer</i>,
during the train phase or the test phase. It's always associated to a <i>DataSet</i>.
It could measure everything on your machine, on the <i>DataSet</i> that
you provide. Here are some examples:
<ul>
<li>
MseMeasurer, which measures the mean squared error of you machine, on the
<i>DataSet</i>.</li>
<li>
ClassMeasurer, which measures the classification error when you have a
classification problem.</li>
</ul>
<p><br>Ok, almost all classes in <b><i>Torch </i></b>are one of these classes...
Easy, no ? If you want to have an idea of all classes which are available
to the public, just have a look on the <a href="manual/index.html">reference
manual</a>...
<br>
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