Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs.
Project 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).
Release Notes
Theano-PyMC 1.0.5 (on deck)
Highlights (since 1.0.4):
First release under new name Theano-PyMC
A total of x people contributed to this release since 1.0.4:
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