<commit_before># these articles don't format well on my kindle or have data visualizations and such


* [Markov Chains Explained Visually](http://setosa.io/ev/markov-chains/)

* [10 R packages I wish I knew about earlier](http://blog.yhat.com/posts/10-R-packages-I-wish-I-knew-about-earlier.html)

* [Python vs R](https://www.dataquest.io/blog/python-vs-r/)

* [The Grammar of Data Science](http://multithreaded.stitchfix.com/blog/2015/03/17/grammar-of-data-science/)

* [11 python libraries](http://blog.yhat.com/posts/11-python-libraries-you-might-not-know.html)

* [WHY YOU SHOULD STOP WORRYING ABOUT DEEP LEARNING AND DEEPEN YOUR UNDERSTANDING OF CAUSALITY INSTEAD](https://yanirseroussi.com/2016/02/14/why-you-should-stop-worrying-about-deep-learning-and-deepen-your-understanding-of-causality-instead/)

* [The hardest parts of data science](https://yanirseroussi.com/2015/11/23/the-hardest-parts-of-data-science/)

* [7 Datasets You've Likely Never Seen Before](http://blog.yhat.com/posts/7-funny-datasets.html)

* [Modern Pandas](http://tomaugspurger.github.io/modern-1.html)

* [Yhat ScienceBox](http://blog.yhat.com/posts/yhat-sciencebox.html)

* [Frequentism and Bayesianism IV: How to be a Bayesian in Python](http://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/)

* [Bayesian Deep Learning](http://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/)

* [A Concrete Introduction to Probability (using Python)](http://nbviewer.jupyter.org/url/norvig.com/ipython/Probability.ipynb)

* [Cross Validation Done Wrong](http://www.alfredo.motta.name/cross-validation-done-wrong/)

* [Useful Unix commands for exploring data](http://datavu.blogspot.ca/2014/08/useful-unix-commands-for-exploring-data.html)

* [How a Math Genius Hacked OkCupid to Find True Love](https://www.wired.com/2014/01/how-to-hack-okcupid/)

* [Manifold learning on handwritten digits](http://scikit-learn.org/stable/auto_examples/manifold/plot_lle_digits.html#sphx-glr-auto-examples-manifold-plot-lle-digits-py)

* [5 Simple Math Problems No One Can Solve](http://www.popularmechanics.com/science/g2816/5-simple-math-problems/)

* [How Distributed Outer Joins on PostgreSQL with Citus Work](https://www.citusdata.com/blog/2016/10/10/outer-joins-in-citus/)

* [How to build a robot that “sees” with $100 and TensorFlow](https://www.oreilly.com/learning/how-to-build-a-robot-that-sees-with-100-and-tensorflow?twitter=@bigdata)

* []()
<commit_msg>Update visual_articles.md<commit_after># these articles don't format well on my kindle or have data visualizations and such


* [Markov Chains Explained Visually](http://setosa.io/ev/markov-chains/)

* [10 R packages I wish I knew about earlier](http://blog.yhat.com/posts/10-R-packages-I-wish-I-knew-about-earlier.html)

* [Python vs R](https://www.dataquest.io/blog/python-vs-r/)

* [The Grammar of Data Science](http://multithreaded.stitchfix.com/blog/2015/03/17/grammar-of-data-science/)

* [11 python libraries](http://blog.yhat.com/posts/11-python-libraries-you-might-not-know.html)

* [WHY YOU SHOULD STOP WORRYING ABOUT DEEP LEARNING AND DEEPEN YOUR UNDERSTANDING OF CAUSALITY INSTEAD](https://yanirseroussi.com/2016/02/14/why-you-should-stop-worrying-about-deep-learning-and-deepen-your-understanding-of-causality-instead/)

* [The hardest parts of data science](https://yanirseroussi.com/2015/11/23/the-hardest-parts-of-data-science/)

* [7 Datasets You've Likely Never Seen Before](http://blog.yhat.com/posts/7-funny-datasets.html)

* [Modern Pandas](http://tomaugspurger.github.io/modern-1.html)

* [Yhat ScienceBox](http://blog.yhat.com/posts/yhat-sciencebox.html)

* [Frequentism and Bayesianism IV: How to be a Bayesian in Python](http://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/)

* [Bayesian Deep Learning](http://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/)

* [A Concrete Introduction to Probability (using Python)](http://nbviewer.jupyter.org/url/norvig.com/ipython/Probability.ipynb)

* [Cross Validation Done Wrong](http://www.alfredo.motta.name/cross-validation-done-wrong/)

* [Useful Unix commands for exploring data](http://datavu.blogspot.ca/2014/08/useful-unix-commands-for-exploring-data.html)

* [How a Math Genius Hacked OkCupid to Find True Love](https://www.wired.com/2014/01/how-to-hack-okcupid/)

* [Manifold learning on handwritten digits](http://scikit-learn.org/stable/auto_examples/manifold/plot_lle_digits.html#sphx-glr-auto-examples-manifold-plot-lle-digits-py)

* [5 Simple Math Problems No One Can Solve](http://www.popularmechanics.com/science/g2816/5-simple-math-problems/)

* [How Distributed Outer Joins on PostgreSQL with Citus Work](https://www.citusdata.com/blog/2016/10/10/outer-joins-in-citus/)

* [How to build a robot that “sees” with $100 and TensorFlow](https://www.oreilly.com/learning/how-to-build-a-robot-that-sees-with-100-and-tensorflow?twitter=@bigdata)

* [How to tame the valley — Hessian-free hacks for optimizing large #NeuralNetworks](https://medium.com/autonomous-agents/how-to-tame-the-valley-hessian-free-hacks-for-optimizing-large-neuralnetworks-5044c50f4b55#.dcoxpog6e)

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