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This is very interesting especially for those users more interested in running deep neural nets than programming deep neural nets. I was initially disinterested because it is missing several important features (at the moment) such as auto encoders and convolutional neural nets. A quick peek inside the example folder revealed the fact that you can specify what you want computed using YAML instead of specifying how to compute it in code means that as long as you're using something Hebel does implement you can quickly experiment with structure and parameters without worrying about programming errors. Very useful for a researcher more interested in playing with the structure of a deep neural net.

It also serializes the model and results to disk by default. This is great for loading up the model later on another, possibly less performant, machine and performing your classification / etc...

Of course PyLearn2 covers this feature set and more, but isn't as easy to get started with.

I'll make an effort to use this when I can, unfortunately my two current projects involve a convolutional neural net and an auto encoder. :(

TL; DR: Specifying structure using YAML instead of coding neural net. Working at a higher level than other libs such as Theano.




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