<story><title>Machine Learning frameworks, libraries and software</title><url>https://github.com/josephmisiti/awesome-machine-learning</url><text></text></story><parent_chain></parent_chain><comment><author>alsocasey</author><text>The R portion needs some serious filling - for starters: you include the Julia wrapper to glmnet, which was originally implemented in R.&lt;p&gt;glmnet - lasso&amp;#x2F;ridge&amp;#x2F;elastic net glm models.&lt;p&gt;e1071 - SVM classifiers.&lt;p&gt;randomForest - random forest classifiers.&lt;p&gt;mixOmics - a good collection of component-based approaches (PCA, ICA, PLS, etc. includes sparse variants of all of the above is feature selection is required).&lt;p&gt;caret - similar to Java&amp;#x27;s Weka.</text></comment>
<story><title>Machine Learning frameworks, libraries and software</title><url>https://github.com/josephmisiti/awesome-machine-learning</url><text></text></story><parent_chain></parent_chain><comment><author>tomaskazemekas</author><text>I suggest adding Torch. &lt;a href=&quot;http://torch.ch/&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;torch.ch&amp;#x2F;&lt;/a&gt;
It is a scientific ML framework written in LuaJIT. Recently it was recommended by Yann LeCun, Director of AI in Facebook. 
&lt;a href=&quot;http://www.reddit.com/r/MachineLearning/comments/25lnbt/ama_yann_lecun/chisdw1&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;www.reddit.com&amp;#x2F;r&amp;#x2F;MachineLearning&amp;#x2F;comments&amp;#x2F;25lnbt&amp;#x2F;ama_...&lt;/a&gt;</text></comment>