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).

    .. _NumPy: http://numpy.scipy.org/


    =============
    Release Notes
    =============

    Theano 1.0.5 (27th of July 2020)
    ================================

    This is a maintenance release of Theano, version ``1.0.5``, with no
    new features, but some deprecation fixes.

    We recommend that everybody update to this version.

    Highlights (since 1.0.4):

     - Theano is now compatible with Python 3.9
     - Fixed many deprecation warnings

    A total of 13 people contributed to this release since ``1.0.4``:

     - 1fish2
     - Frederic Bastien
     - Rebecca Palmer
     - Miro Hrončok
     - Dan Foreman-Mackey
     - Adrian Seyboldt
     - abergeron
     - Tim Gates
     - Tim Odonnell
     - Robert P. Goldman
     - Duc Nguyen
     - Igor Varfolomeev
     - Thomas Wiecki

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