# Welcome

Theano is a Python library that allows you to define, optimize, and
evaluate mathematical expressions involving multi-dimensional
arrays efficiently. Theano features:

**tight integration with NumPy** – Use numpy.ndarray in Theano-compiled functions.
**transparent use of a GPU** – Perform data-intensive calculations up to 140x faster than with CPU.(float32 only)
**efficient symbolic differentiation** – Theano does your derivatives for function with one or many inputs.
**speed and stability optimizations** – Get the right answer for `log(1+x)` even when `x` is really tiny.
**dynamic C code generation** – Evaluate expressions faster.
**extensive unit-testing and self-verification** – Detect and diagnose many types of mistake.

Theano has been powering large-scale computationally intensive
scientific investigations since 2007. But it is also approachable
enough to be used in the classroom (IFT6266 at the University of
Montreal).

# News

You can watch a quick (20 minute) introduction to Theano given as a talk at SciPy 2010 via streaming (or downloaded) video:

# Download

Theano is now available on PyPI, and can be installed via `easy_install
Theano`, `pip install Theano` or by downloading and unpacking the tarball
and typing `python setup.py install`.

Those interested in bleeding-edge features should obtain the latest development
version, available via:

git clone git://github.com/Theano/Theano.git

You can then place the checkout directory on your `$PYTHONPATH` or use
`python setup.py develop` to install a `.pth` into your `site-packages`
directory, so that when you pull updates via Git, they will be
automatically reflected the “installed” version. For more information about
installation and configuration, see *installing Theano*.

# Citing Theano

If you use Theano for academic research, you are highly encouraged (though not
required) to cite the following two papers:

- F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. Goodfellow,
A. Bergeron, N. Bouchard, D. Warde-Farley and Y. Bengio.
“Theano: new features and speed improvements”.
NIPS 2012 deep learning workshop. (BibTex)
- J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R.
Pascanu, G. Desjardins, J. Turian, D. Warde-Farley and Y.
Bengio. “Theano: A CPU and GPU Math Expression Compiler”.
*Proceedings of the Python for Scientific Computing Conference (SciPy)
2010. June 30 - July 3, Austin, TX* (BibTeX)

Theano is primarily developed by academics, and so citations matter a lot to
us. As an added benefit, you increase Theano’s exposure and potential user
(and developer) base, which is to the benefit of all users of Theano. Thanks
in advance!

See our *Theano Citation Policy* for details.

# Documentation

Roughly in order of what you’ll want to check out:

You can download the latest PDF documentation, rather than reading it online.

Check out how Theano can be used for Machine Learning: Deep Learning Tutorials.

Theano was featured at SciPy 2010.