===========================
 Announcing Theano 0.3.1rc3
===========================

This is a bug/crash fix and small feature release.
The upgrade is recommended for everybody.

For those using the bleeding edge version in the
mercurial repository, we encourage you to update to the `0.3.1rc3` tag.


Deleting old cache
------------------

Since the default path of the cache directory for compiled object
changed, we encourage you to delete the previous one.

The easiest way to do that is to execute:
    python -c 'import theano; print theano.config.base_compiledir'
and then call "rm -rf" on the returned result.

A new cache directory will then be created next time you import theano.


What's New
----------

[Include the content of NEWS.txt here]


Download
--------

You can download Theano from http://pypi.python.org/pypi/Theano.

Description
-----------

Theano is a Python library that allows you to define, optimize, and
efficiently evaluate mathematical expressions involving
multi-dimensional arrays. It is built on top of NumPy. Theano
features:

 * tight integration with NumPy: a similar interface to NumPy's.
  numpy.ndarrays are also used internally in Theano-compiled functions.
 * transparent use of a GPU: perform data-intensive computations up to
  140x faster than on a CPU (support for float32 only).
 * efficient symbolic differentiation: Theano can compute derivatives
  for functions of one or many inputs.
 * speed and stability optimizations: avoid nasty bugs when computing
  expressions such as log(1+ exp(x) ) for large values of x.
 * dynamic C code generation: evaluate expressions faster.
 * extensive unit-testing and self-verification: includes tools for
  detecting and diagnosing bugs and/or potential problems.

Theano has been powering large-scale computationally intensive
scientific research since 2007, but it is also approachable
enough to be used in the classroom (IFT6266 at the University of Montreal).

Resources
---------

About Theano:

http://deeplearning.net/software/theano/

About NumPy:

http://numpy.scipy.org/

About Scipy:

http://www.scipy.org/

Machine Learning Tutorial with Theano on Deep Architectures:

http://deeplearning.net/tutorial/

Acknowledgments
---------------

I would like to thank all contributors of Theano. For this particular
release, the people who have helped resolve many outstanding issues:
(in alphabetical order) Frederic Bastien, Arnaud Bergeron, James
Bergstra, Josh Bleecher Snyder, Olivier Delalleau, Guillaume
Desjardins, Dumitru Erhan, Ian Goodfellow, Pascal Lamblin, Razvan
Pascanu and Francois Savard and David Warde-Farley.

Also, thank you to all NumPy and Scipy developers as Theano builds on
its strength.

All questions/comments are always welcome on the Theano
mailing-lists ( http://deeplearning.net/software/theano/ )


