10 Reasons Python Rocks for Research (And a Few Reasons it Doesn’t) — Hoyt Koepke

The following is an account of my own experience with Python. Because that experience has been so positive, it is an unabashed attempt to promote the use of Python for general scientific research and development. About four years ago, I dropped MATLAB in favor of Python as my primary language for coding research projects. This article is a personal account of how rewarding I have found that experience. As I describe in the next sections, the variety and quality of Python’s features has spoiled me. To clarify, I am not advocating just Python as the perfect scientific programming environment; I am advocating Python plus a handful of mature 3rd-party open source libraries, namely Numpy/Scipy for numerical operations, Cython for low-level optimization, IPython for interactive work, and MatPlotLib for plotting. Holistic Language Design I once believed that the perfect language for research was one that allowed concise and direct translation from notepad scribblings to code. Readability gives

Difference between these Python machine-learning libraries What can MATLAB do that R cannot do UC BerkeleyX: CS188.1x: Artificial Intelligence *Note - This is an Archived course* This is a past/archived course. At this time, you can only explore this course in a self-paced fashion. Certain features of this course may not be active, but many people enjoy watching the videos and working with the materials. Make sure to check for reruns of this course. CS188.1x is a new online adaptation of the first half of UC Berkeley's CS188: Introduction to Artificial Intelligence. Artificial intelligence is already all around you, from web search to video games. CS188.1x focuses on Behavior from Computation. Note: This is a past/archived course.

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