Machine Learning Inside
Channel: Linux.conf.au 2011 -- Brisbane, Australia
Date: 2014-11-23

Presenter(s): Christfried Webers
URL: http://2011.linux.conf.au/programme/schedule/view_talk/155

A huge amount of raw data has now become available through the
Internet, the minaturisation of sensors and cheap storage. Statistical
Machine Learning uses statistical models to find and extract patterns
in these large data sets. For example, there exist now algorithms
which learn from a set of photographs how natural scenes look and
apply these knowledge to remove distortions and noise from unseen new
photos.

The Elefant project (Efficient Learning, Large-scale Inference, and
Optimisation Toolkit) written in Python/C/C++ provides an API for
efficient Machine Learning algorithms and a component architecture
with a graphical user interface (GUI) aiming to support application
developers and researchers by
- providing a data flow model with data converters for a variety of
  data formats (e.g. CVS, LibSVM, Matlab)
- incorporating a large number of Machine Learning algorithms
- linking with major scientific libraries (BLAS, LAPACK, PETSc)
- allowing fast prototyping via the GUI
- producing standalone executable Python code for an experiment
  created via the GUI

The evolving multicore architectures provide interesting challenges as
algorithms working with tightly coupled structures (graphs, networks)
should efficiently run from processors with a few to hundred of cores.
Further speedups for some parts of the computations can be gained by
utilising GPUs although it remains an open question how portable such
solutions will be.

The Elefant Toolkit is provided under the Mozilla Public License and
runs under Linux, Mac OS and Windows.

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