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The primary goal of this talk is twofold: to evaluate the need of data mining and introduce some very cool, simple yet powerful machine learning techniques to audience such as classification, clustering, collaborative filtering, recommendation etc in your Python web applications. This talk will conclude with some explanation and limitations of machine learning algorithms.
Basic knowledge of Python is sufficient. However some experience with Django, meshups, machine learning or data hunger is encouraged. All talk material and django apps will be available after talk.
Python is an accepted high-level scripting language with a growing community in academia and industry. It is used in a lot of scientific applications in many different scientific fields and in more and more industries, for example, in engineering or life science). In all fields, the use of Python for high-performance and parallel computing is increasing. Several organizations and companies are providing tools or support for Python development. This includes libraries for scientific computing, parallel computing, and MPI. Python is also used on many core architectures and GPUs, for which specific Python interpreters are being developed. A related topic is the performance of the various interpreter and compiler implementations for Python.
The talk gives an overview of Python’s use in HPC and Scientific Computing and gives information on many topics, such as Python on massively parallel systems, GPU programming with Python, scientific libraries in Python, and Python interpreter performance issues. The talk will include examples for scientific codes and applications from many domains.
20th–26th June 2011