[SciPy-User] Announcing Theano 1.0.1

[Steven Bocco]

 Announcing Theano 1.0.1

This is a maintenance release of Theano, version 1.0.1, with no new
features, but some important bug fixes.

Upgrading to Theano 1.0.1 is recommended for everyone. For those using the
bleeding edge version in the git repository, we encourage you to update to
the rel-1.0.1 tag.
What's New

Highlights (since 1.0.0):


   - Fixed compilation and improved float16 support for topK on GPU
      - *NB*: topK support on GPU is experimental and may not work for
      large input sizes on certain GPUs
   - Fixed cuDNN reductions when axes to reduce have size 1
   - Attempted to prevent re-initialization of the GPU in a child process
   - Fixed support for temporary paths with spaces in Theano initialization
   - Spell check pass on the documentation

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 much
   faster than on a CPU.
   - 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. Since release 1.0.0, many
people have helped, notably (in alphabetical order):


   - Arnaud Bergeron
   - Edward Betts
   - Frederic Bastien
   - Sam Johnson
   - 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 )


-- 
Steven Bocco
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scipy-user/attachments/20171207/7402fdd8/attachment.html>