Show HN: Kur by Deepgram (YC S16) – Deep Learning Made Easy

Deepgram is open sourcing Kur (http://kur.deepgram.com)! Kur is the worlds first descriptive Deep Learning software. Think of a model, describe it in a simple YAML or JSON file, and train to get state-of-the-art results. There's no need to code.

Why we built Kur: Prototyping DNNs is a slow process. Most people doing deep learning want to iterate and try out different model architectures and learn from others. It's hard to do this using barebones backends like TensorFlow/Theano or even the higher abstraction of software like Keras.

Kur is not speech specific. It can be used for images (we supply two examples), speech (we supply one example and DG is open sourcing a new audio dataset with it, the DEEPGRAM10), text, etc.

There are CNN layers, RNN, dense, dropout, batch norm, etc. to pick and choose from. The best part? Kur does all the plumbing! You want one input but two outputs? Not a problem, describe that model in Kur!

We're really pumped to be releasing Kur and would love to answer questions if you've got em.

Thanks! Deepgram AI Research Team

http://kur.deepgram.com http://github.com/deepgram/kur http://kurhub.com

Try out the examples, tweak the models, and let us know how you get on!

Also, we're hanging around on gitter if you want real time advice :)

Great idea! However installation instructions are a little misleading. It says I only need Python 3, while in reality I need a whole bunch of things to be installed already. Is there a list of all requirements?

Also, does it work on Windows? "DL for dummies" code should run on Windows :)

Nice job! I want to see another RNN net generate the yaml configs after we have collected enough of them for training. :)

There have been a few frameworks that take this declarative approach; DSSTNE, Twitter's internal framework and probably others.

DSSTNE had a clearish reason for doing so: automatic model-parallel training. Twitter wanted something simpler than Torch for most of their devs to use.

But I'm not really seeing why you guys did it.