============================================================================================================ `MILA will stop developing Theano <https://groups.google.com/d/msg/theano-users/7Poq8BZutbY/rNCIfvAEAwAJ>`_. The PyMC developers are continuing Theano development in a `fork <https://github.com/pymc-devs/theano-pymc>`_. ============================================================================================================ To install the package, see this page: http://deeplearning.net/software/theano/install.html For the documentation, see the project website: http://deeplearning.net/software/theano/ Related Projects: https://github.com/Theano/Theano/wiki/Related-projects It is recommended that you look at the documentation on the website, as it will be more current than the documentation included with the package. In order to build the documentation yourself, you will need sphinx. Issue the following command: :: python ./doc/scripts/docgen.py Documentation is built into ``html/`` The PDF of the documentation can be found at ``html/theano.pdf`` ================ DIRECTORY LAYOUT ================ ``Theano`` (current directory) is the distribution directory. * ``Theano/theano`` contains the package * ``Theano/theano`` has several submodules: * ``gof`` + ``compile`` are the core * ``scalar`` depends upon core * ``tensor`` depends upon ``scalar`` * ``sparse`` depends upon ``tensor`` * ``sandbox`` can depend on everything else * ``Theano/examples`` are copies of the example found on the wiki * ``Theano/benchmark`` and ``Theano/examples`` are in the distribution, but not in the Python package * ``Theano/bin`` contains executable scripts that are copied to the bin folder when the Python package is installed * Tests are distributed and are part of the package, i.e. fall in the appropriate submodules * ``Theano/doc`` contains files and scripts used to generate the documentation * ``Theano/html`` is where the documentation will be generated
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.
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