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(This article was first published on <strong><a href="https://www.rstudio.com/rviews/2016/11/25/some-technical-reading-jbr/"> RStudio</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p>by Joseph Rickert</p>
<p>I am ever-optimistic that on the weekend I will have enough time and brainpower to do some serious technical reading. The following are five articles on my list. I hope that at least one of them resonates with you too. Happy reading.</p>
<p><strong>1.</strong> In this <a target="_blank" rel="nofollow" href="http://www.nature.com/articles/ncomms12474">paper from Nature Communications</a>, Kun-Hsing Yu et al. use machine learning to diagnose cancer. The authors write:</p>
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<div>“We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors with stage I adenocarcinoma (P&lt;0.003) or squamous cell carcinoma (P=0.023) in the TCGA data set.”</div>
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<p>Figure <strong>a</strong> shows ROC curves for classifying lung <a target="_blank" rel="nofollow" href="http://www.cancercenter.com/terms/adenocarcinoma/">adenocarcinoma</a> versus adjacent normal tissue.</p>
<p><img alt="roc-curves-adenocarcinoma" class="wp-image-14339" src="https://i0.wp.com/www.rstudio.com/wp-content/uploads/2016/11/ROC-Curves-adenocarcinoma-.png?resize=456%2C274" data-recalc-dims="1"></p>
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<p><strong>2.</strong> John Mount and Nina Zumel describe <a target="_blank" rel="nofollow" href="https://docs.google.com/document/d/1J-4SygKYizo_nRX5UApjZ8I4_GuhlKK4yEcHDXrJp8o/edit">Why you should re-encode high dimensional categorical variables.</a></p>
<p><strong>3.</strong> Jamie Ashander delves into the details of the <a target="_blank" rel="nofollow" href="http://www.ashander.info/posts/2015/04/D-RUG-mixed-effects-viz/?utm_content=buffer9fd4c&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer">statistics of General Linear Mixed Models</a>, and provides the R code to help you build your own models.</p>
<p><strong>4.</strong> Sebastian Ruder does a fine job of explaining the math in his post<a target="_blank" rel="nofollow" href="http://sebastianruder.com/optimizing-gradient-descent/index.html#nesterovacceleratedgradient">An overview of gradient descent optimization algorithms</a></p>
<div align="center"><iframe height="480px" width="620px" src="https://www.rstudio.com/wp-content/uploads/2016/11/saddle_point_evaluation_optimizers.gif"></iframe></div>
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<p><strong>5.</strong> Iwana and Uchida use deep learning to do something your mother told you never to do: <a target="_blank" rel="nofollow" href="https://arxiv.org/pdf/1610.09204v1.pdf">Judging a Book by Its Cover</a></p>
<p><img alt="bookbycover" class="wp-image-14338" src="https://i0.wp.com/www.rstudio.com/wp-content/uploads/2016/11/BookbyCover.png?resize=456%2C364" data-recalc-dims="1">
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