Description:

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.
* symbolic differentiation – Let Theano do your derivatives.
* 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).

Theano has been used primarily to implement large-scale deep learning algorithms. To see how, see the Deep Learning Tutorials (http://www.deeplearning.net/tutorial/)

Changes to previous version:

Theano 0.8 (21th of March, 2016)

We recommend to everyone to upgrade to this version.

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

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Linux, Macosx, Windows

Data Formats: Agnostic

Tags: Python, Cuda, Gpu, Symbolic Differentiation, Numpy

Archive: download here

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