============================ Announcing Theano 0.9.0rc3 ============================
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 0.9.0rc2, you are highly encouraged to update to 0.9.0rc3. For those using the bleeding edge version in the git repository, we encourage you to update to the `rel-0.9.0rc3` tag. What's New ---------- Highlights: - Graph clean up and faster compilation - New Theano flag conv.assert_shape to check user-provided shapes at runtime (for debugging) - Fix overflow in pooling - Warn if taking softmax over broadcastable dimension - Removed old files not used anymore - Test fixes and crash fixes - New GPU back-end: - Removed warp-synchronous programming, to get good results with newer CUDA drivers 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): - Arnaud Bergeron - Florian Bordes - Frederic Bastien - Jan Schlüter - Pascal Lamblin 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 ) -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
