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prepare for v0.1.0
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NEWS.md

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# News for Mocha Development
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## v0.1.0 2015.10.10
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To celebrate the release of Julia v0.4 and increased involvement from the community of the development of Mocha.jl, we decided to have a v0.1.0 release.
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* Infrastructure
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* Backward compatibility with Julia v0.3, and Julia v0.4 compatability
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* Solver refactoring (@benmoran, @CarloLucibello)
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* Adam Solver (@benmoran)
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* Improved compatibility on Windows (@droidicus)
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* Network
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* Leaky ReLU (@stjanovitz)
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* Shuffling support for memory-data layer
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* `DecayOnValidation` now support square-loss like criterions
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* Variational Auto-Encoder and related layers: `RandomNormal`, `BinaryCrossEntropyLossLayer`, `GaussianKLLoss`, and `Exponential` neuron type (@benmoran)
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* SVM related layers: `HingeLossLayer` and `BinaryAccuracyLayer` (@nstiurca)
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* `OrthogonalInitializer` (@benmoran)
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* Documentation
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* Various typos and improvements from the community
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* MNIST Variational Auto-Encoder example (@benmoran)
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## v0.0.9 2015.07.20
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* Infrastructure

docs/conf.py

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# built documents.
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#
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# The short X.Y version.
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version = '0.0.9'
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version = '0.1.0'
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# The full version, including alpha/beta/rc tags.
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release = '0.0.9'
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release = '0.1.0'
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# The language for content autogenerated by Sphinx. Refer to documentation
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# for a list of supported languages.

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