Thanks for bringing up other frameworks in DL! Amazon's DSSTNE is restrictive, but it's great for their purposes. On the DSSTNE GitHub you can see statements li

Thanks for bringing up other frameworks in DL! Amazon's DSSTNE is restrictive, but it's great for their purposes. On the DSSTNE GitHub you can see statements like: "DSSTNE currently only supports Fully Connected layers ..." Kur supports the cutting edge: like CNN/RNN. "DSSTNE Engine works with data only in NetCDF format." Kur supports the data that you have on hand. You can see in the tutorial (http://kur.deepgram.com/tutorial.html http://kur.deepgram.com/tutorial.html) how easy it is to send brand new data in the familiar Python pickle object. These are the kind of GOTCHAs that people doing deep learning run into all the time. And they are a major time suck. Kur relieves you of those duties so you can work on more interesting parts, like trying novel models :). We're so glad DL tools are coming out. But there's still tons of progress to be made and Kur is one step along that path—making the user experience more efficient and enjoyable.