12. Other Resources¶

Scikit-learn astronomy tutorial: http://astroML.github.com/sklearn_tutorial: This is an online tutorial which introduces the scikit-learn interface and the fundamental ideas of machine learning, and applies them within the context of astronomical data analysis. It includes several videos of the tutorial being presented at conferences.
Scikit-learn documentation: http://scikit-learn.org: The scikit-learn documentation is extensive, and features detailed information on many of the routines used in the astroML examples.
Matplotlib documentation: http://matplotlib.org: Matplotlib enables all the plots on this website, and its website includes extensive documentation and examples. See especially the example gallery.
IPython documentation: http://ipython.org: IPython is is an enhanced interactive python interpreter, and also provides a framework for parallel computing and cross-platform sharing of code and results. It is an essential tool for any scientific python user.
Python scientific lecture notes: http://scipy-lectures.github.com/: These notes cover some of the basics of scientific python, including numpy, scipy, and matplotlib. From there they move into an in-depth exploration of more advanced and specialized topics in scientific computing.