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===========================
Announcing Theano 0.5
===========================
## You can select and adapt one of the following templates.
## Basic text for major version release:
This is a release for a major version, with lots of new
features, bug fixes, and some interface changes (deprecated or
potentially misleading features were removed).
Upgrading to Theano 0.5 is recommended for everyone, but you should first make
sure that your code does not raise deprecation warnings with Theano 0.4.1.
Otherwise, in one case the results can change. In other cases, the warnings are
turned into errors (see below for details).
For those using the bleeding edge version in the
git repository, we encourage you to update to the `0.5` tag.
## Basic text for major version release candidate:
This is a release candidate for a major version, with lots of new
features, bug fixes, and some interface changes (deprecated or
potentially misleading features were removed).
The upgrade is recommended for developpers who want to help test and
report bugs, or want to use new features now. If you have updated
to 0.5rc1, you are highly encouraged to update to 0.5rc2.
For those using the bleeding edge version in the
git repository, we encourage you to update to the `0.5rc2` tag.
## Basic text for minor version release:
TODO
## Basic text for minor version release candidate:
TODO
What's New
----------
[Include the content of NEWS.txt here]
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):
Hani Almousli, Frédéric Bastien, Justin Bayer, Arnaud Bergeron, James
Bergstra, Valentin Bisson, Josh Bleecher Snyder, Yann Dauphin, Olivier
Delalleau, Guillaume Desjardins, Sander Dieleman, Xavier Glorot, Ian
Goodfellow, Philippe Hamel, Pascal Lamblin, Eric Laufer, Grégoire
Mesnil, Razvan Pascanu, Matthew Rocklin, Graham Taylor, Sebastian Urban,
David Warde-Farley, and Yao Li.
I would also like to thank users who submitted bug reports, notably:
Nicolas Boulanger-Lewandowski, Olivier Chapelle, Michael Forbes, Timothy
Lillicrap, and John Salvatier.
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 )