Fwd: Announcing Theano 0.9.0rc2

[Steven Bocco]

============================

 Announcing Theano 0.9.0rc2
============================

This is a release candidate for a major version with many bug fixes and improvements.

The upgrade is recommended for developers who want to help test and
report bugs, or want to use new features now.  If you have updated
to 0.9.0rc1, you are highly encouraged to update to 0.9.0rc2.

For those using the bleeding edge version in the
git repository, we encourage you to update to the `rel-0.9.0rc2` tag.


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

Highlights:
 - Fixed dnn conv grad issues
 - Allowed pooling of empty batch
 - Use of 64-bit indexing in sparse ops to allow matrix with more then 2\ :sup:`31`\ -1 elements.
 - Removed old benchmark directory
 - Crash fixes, bug fixes, warnings improvements, and documentation update


Download and Install
--------------------

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

Installation instructions are available at
http://deeplearning.net/software/theano/install.html

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/

Theano-related projects:

http://github.com/Theano/Theano/wiki/Related-projects

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, many people have helped, notably (in alphabetical order):

 - David Bau
 - Frederic Bastien
 - Lucas Beyer
 - Micah Bojrab
 - Michael Harradon
 - Pascal Lamblin
 - Rebecca N. Palmer
 - Simon Lefrancois
 - Steven Bocco

Also, thank you to all NumPy and Scipy developers as Theano builds on
their strengths.

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