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Après Nipdev 37 où nous avons vu la théorie des algorithmes de Machine Learning <a href="http://twitter.com/antoined" target="_blank">@antoined</a> nous décrit quelques librairies Pythons utilisées pour implémenter ces algorithmes.
Quelques liens évoqués dans l&rsquo;émission :
<ul>
<li><a href="http://deeplearning.net/software/theano/#download" target="_blank">Theano</a></li>
<li><a href="http://deeplearning.net/tutorial/" target="_blank">Liens tuto deep learning</a></li>
<li><a href="http://keras.io/models/" target="_blank">Keras</a></li>
<li><a href="https://www.tensorflow.org/" target="_blank">Google TensorFlow</a></li>
<li><a href="http://www.numpy.org/" target="_blank">Numpy</a></li>
<li><a href="http://pandas.pydata.org/" target="_blank">Pandas</a></li>
<li><a href="http://caret.r-forge.r-project.org/" target="_blank">R “caret”</a></li>
<li><a href="https://www.udacity.com//course/viewer#!/c-ud730/l-6370362152/m-6379811815" target="_blank">Cours Udacity sur le ML</a></li>
<li><a href="https://openai.com/blog/introducing-openai/" target="_blank">Open AI by Elon Musk &amp; Co</a></li>
</ul>