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@ -353,6 +353,7 @@ on MNIST digits[DEEP LEARNING]
* [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - Book/iPython notebooks on Probabilistic Programming in Python
* [MLlib in Apache Spark](http://spark.apache.org/docs/latest/mllib-guide.html) - Distributed machine learning library in Spark
* [scikit-learn](http://scikit-learn.org/) - A Python module for machine learning built on top of SciPy.
* [astroML](http://www.astroml.org/) - Machine Learning and Data Mining for Astronomy.
* [graphlab-create](http://graphlab.com/products/create/docs/) - A library with various machine learning models (regression, clustering, recommender systems, graph analytics, etc.) implemented on top of a disk-backed DataFrame.
* [BigML](https://bigml.com) - A library that contacts external servers.
* [pattern](https://github.com/clips/pattern) - Web mining module for Python.
@ -388,7 +389,6 @@ on MNIST digits[DEEP LEARNING]
* [SymPy](https://github.com/sympy/sympy) - A Python library for symbolic mathematics.
* [statsmodels](https://github.com/statsmodels/statsmodels) - Statistical modeling and econometrics in Python.
* [astropy](http://www.astropy.org/) - A community Python library for Astronomy.
* [astroML](http://www.astroml.org/) - Machine Learning and Data Mining for Astronomy.
* [matplotlib](http://matplotlib.org/) - A Python 2D plotting library.
* [bokeh](https://github.com/ContinuumIO/bokeh) - Interactive Web Plotting for Python.
* [plotly](https://plot.ly/python) - Collaborative web plotting for Python and matplotlib.