Announcing Theano 0.6rc3

[Frédéric Bastien]

===========================
 Announcing Theano 0.6rc3
===========================

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 everybody.

For those using the bleeding edge version in the
git repository, we encourage you to update to the `rel-0.6rc3` tag.

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

 * Windows related fixes

 * Speed-ups.
 * Crash fixes.
 * A few small interface changes.
 * GPU memory leak fix.
 * A few corner cases fixes without incidence.
 * More Theano determinism
 * tensor.{dot,tensordot} more complete/faster/GPU friendly.
 * tensor.tensordot now support Rop/Lop
 * tensor.dot support n-dimensional inputs as NumPy
 * To support more NumPy syntax:
     * Add theano.tensor.take()
     * Add a_tensor_variable.{sort,dot,std,argmin,argmax,argsort,clip,conj,conjugate,repeat,round,trace,real,imag,take}


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):

abalkin
Rami Al-Rfou'
Frederic Bastien
James Bergstra
Olivier Delalleau
Guillaume Desjardins
Amir Elaguizy
Ian Goodfellow
Eric Hunsberger
Vivek Kulkarni
Pascal Lamblin
Jeremiah Lowin
Razvan Pascanu
David Warde-Farley

I would also like to thank users who submitted bug reports and suggestion.

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 )