======================== Announcing Theano 0.8.0 ======================== 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). 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-0.8.0` tag. What's New ---------- Highlights:« - Python 2 and 3 support with the same code base - Faster optimization - Integration of CuDNN for better GPU performance - Many Scan improvements (execution speed up, ...) - optimizer=fast_compile moves computation to the GPU. - Better convolution on CPU and GPU. (CorrMM, cudnn, 3d conv, more parameter) - Interactive visualization of graphs with d3viz - cnmem (better memory management on GPU) - BreakpointOp - Multi-GPU for data parallism via Platoon ( https://github.com/mila-udem/platoon/) - More pooling parameter supported - Bilinear interpolation of images - New GPU back-end: * Float16 new back-end (need cuda 7.5) * Multi dtypes * Multi-GPU support in the same process A total of 141 people contributed to this release, please see the end of NEWS.txt for the complete list. If you are among the authors and would like to update the information, please let us know. 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 deep learning classes. All questions/comments are always welcome on the Theano mailing-lists ( http://deeplearning.net/software/theano/#community ) Frédéric