Hebel

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05-22-2014

* Added setup.py
* Added to PyPi

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05-08-2014

* Windows compatibility (Thanks to [Wainberg](https://github.com/Wainberg))
* CUDA 4.x is no longer supported, please upgrade to CUDA 5 or CUDA 6
* All initialization is now handled through `hebel.init()`. No need to
initialize PyCUDA separately anymore.
* `LogisticLayer` has been renamed to `SoftmaxLayer`. `LogisticLayer`
now does binary classification while `SoftmaxLayer` is for
multiclass classification.
* Framework for cross-validation.
* When `ProgressMonitor` has `save_interval=None`, then only the
currently best model is serialized. If it is a positive integer,
then regular snapshots of the model are stored with that frequency.

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01-01-2014

* Removed dependency on scikits.cuda (this should make Hebel
compatible with Windows, but I couldn't test that yet)

* Serious speed-ups by avoiding freeing and reallocating memory for
temporary objects. Previously, many temporary gpuarrays were
reallocated in every single minibatch and then discarded, which was
very inefficient. By using persistent objects for temporary objects
across minibatches and some other improvements such as doing more
computations in-place, a roughly 2x speed-up could be realised.