code.html

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<!-- Open-source implementations of popular deep learning techniques with -->
<!-- applications to NLP. While this is research code (and usual caveats -->
<!-- apply), it has been used in industrial teams outside of our group, and -->
<!-- licenses are included in the repos. -->
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<!-- <h3>Projects</h3> -->

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{{project.authors}}.<br>
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{% if project.pdf %}<a class="icon pdf label label-info" href="{{project.pdf}}">pdf</a> {% endif %}
{% if project.models %}<a class="icon slides label label-warning" href="{{project.models}}">models</a>{% endif %}
{% if project.code %} <a class="icon slides label label-success label-warning" href="{{project.code}}">code</a>{% endif %}


<br>

</td></tr>

{% endfor %}
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<h3>Tutorials</h3>


If you are new to learning Torch we have a set of tutorial prepared as
part of <a href="http://cs287.fas.harvard.edu">CS287</a> a graduate
class on ML in NLP. These notebooks, prepared by Sam Wiseman and
Saketh Rama, assume basic familiarity with the core aspects of Torch,
and move quickly to advanced topics such memory usage, the details of
the nn module, and recurrent neural networks.
<br><br>


<table class="table table-striped">
  <tr>
    <td>
      <a href="http://nbviewer.jupyter.org/urls/cs287.github.io/Lectures/notebooks/TorchTutorial.ipynb" >General Torch Notes</a>
      </td>
  </tr>
  <tr>
    <td>
    <a href="http://nbviewer.jupyter.org/urls/cs287.github.io/Lectures/notebooks/NNTutorial.ipynb" >Torch nn module Notes</a>
    </td>
  </tr>
  <tr>
    <td> <a href="http://nbviewer.jupyter.org/github/CS287/Lectures/blob/gh-pages/notebooks/ElementRNNTutorial.ipynb" >Torch rnn Notes</a> </td>
  </tr>
  <tr>
    <td><a href="https://CS287.github.io/Lectures/dpnlp.pdf" >Torch and Dynamic Programming </a></td>
  </tr>
</table>