[Numpy-discussion] Theano 0.6 released

[Frédéric Bastien]

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

We recommend that everybody update to this version.


Highlights (since 0.6rc5):
 * Last release with support for Python 2.4 and 2.5.
 * We will try to release more frequently.
 * Fix crash/installation problems.
 * Use less memory for conv3d2d.

0.6rc4 skipped for a technical reason.

Highlights (since 0.6rc3):
 * Python 3.3 compatibility with buildbot test for it.
 * Full advanced indexing support.
 * Better Windows 64 bit support.
 * New profiler.
 * Better error messages that help debugging.
 * Better support for newer NumPy versions (remove useless warning/crash).
 * Faster optimization/compilation for big graph.
 * Move in Theano the Conv3d2d implementation.
 * Better SymPy/Theano bridge: Make an Theano op from SymPy expression
and use SymPy c code generator.
 * Bug fixes.

Change from 0.6rc5:
 * Fix crash when specifing march in cxxflags Theano flag. (Frederic
B., reported by FiReTiTi)
 * code cleanup (Jorg Bornschein)
 * Fix Canopy installation on windows when it was installed for all
users: Raingo
 * Fix Theano tests due to a scipy change. (Frederic B.)
 * Work around bug introduced in scipy dev 0.14. (Frederic B.)
 * Fix Theano tests following bugfix in SciPy. (Frederic B., reported
by Ziyuan Lin)
 * Add Theano flag cublas.lib (Misha Denil)
 * Make conv3d2d work more inplace (so less memory usage) (Frederic
B., repoted by Jean-Philippe Ouellet)

See https://pypi.python.org/pypi/Theano for more details.


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 (since 0.5), many people have helped, notably:


Frederic Bastien
Pascal Lamblin
Ian Goodfellow
Olivier Delalleau
Razvan Pascanu
abalkin
Arnaud Bergeron
Nicolas Bouchard +
Jeremiah Lowin +
Matthew Rocklin
Eric Larsen +
James Bergstra
David Warde-Farley
John Salvatier +
Vivek Kulkarni +
Yann N. Dauphin
Ludwig Schmidt-Hackenberg +
Gabe Schwartz +
Rami Al-Rfou' +
Guillaume Desjardins
Caglar +
Sigurd Spieckermann +
Steven Pigeon +
Bogdan Budescu +
Jey Kottalam +
Mehdi Mirza +
Alexander Belopolsky +
Ethan Buchman +
Jason Yosinski
Nicolas Pinto +
Sina Honari +
Ben McCann +
Graham Taylor
Hani Almousli
Ilya Dyachenko +
Jan Schlüter +
Jorg Bornschein +
Micky Latowicki +
Yaroslav Halchenko +
Eric Hunsberger +
Amir Elaguizy +
Hannes Schulz +
Huy Nguyen +
Ilan Schnell +
Li Yao
Misha Denil +
Robert Kern +
Sebastian Berg +
Vincent Dumoulin +
Wei Li +
XterNalz +


A total of 51 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.

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 )