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Advanced Scientific Programming in Python

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If you have a question about this talk, please contact Shakir Mohamed.

The Python programming language offers various features that make it an invaluable part of the scientific programmer’s toolbox. These features include the expressiveness of a modern object oriented language, a large library of functions for statistics, signal processing, numerical optimisation, linear algebra, etc., and active developer and user communities.

For the uninitiated, the talk will start with a brief introduction to the Python programming language and the SciPy library of functions. Thereafter I will present highlights of the recent Advanced Scientific Programming in Python course held in Trento, Italy. A wide range of topics will be covered—including multidimensional arrays, working with massive datasets, source and version control, parallel processing (in shared memory, computing cluster, and GPU flavours), interleaving C++ and Python code, profiling, debugging, and test-based development. 90 minutes is not enough time to do more than scratch the surface of each of these topics, so I will aim to motivate the importance of each of the topics—using examples where possible—and provide references for those who wish to learn more.

Should you wish to do some pre-reading on Python, a friendly introduction with the focus on scientific programming can be found at https://portal.g-node.org/python-autumnschool/_media/pythonscientific.pdf

This talk is part of the Machine Learning Reading Group @ CUED series.

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