[jay-anderson]

Any suggestions on where to start learning how to implement this? I understand some of the high level concepts (and took an intro AI class years ago - probably not terribly useful), but some of them are very much over my head (e.g. 2.2 Softmax Distributions and 2.3 Gated Activation Units) and some parts of the paper feel somewhat hand-wavy (2.6 Context Stacks). Any pointers would be useful as I attempt to understand it. (EDIT: section numbers refer to their paper)

[visarga]

Best advice is to wait for a version to pop up on github. It's hard to implement such a paper as a beginner.

[datenwolf]

Well, I think since we now have frameworks for doing this kind of stuff (Tensorflow and similar) the barrier of entry is much, much lower. Also the computing power required to build the models can be found in commodity GPUs.

On a hunch I'd say an absolute beginner may be able good results with these tools, just not as quickly as experts on the field who already know how to use the tools properly. That's why I'm going to wait for something to pop up on GitHub, because I have zero practical experience with these things, but I can read these papers comfortably without the need to look up every other term.

There are a number of applications I'd like to throw at deep learning to see how it performs. Most notably I'd like to see how well a deep learning system can extract feature from speckle images. At the moment you have to average out the speckles from ultrasound or OCT images before you can feed it to a feature recognition system. Unfortunately this kind of averaging eliminates certain information you might want to process further down the line.

[jm547ster]

Agreed there's a lot of breath here, I'm coming from the opposite end with some experience in "manual" concatenative speech synthesis and very little in the ML area, you'd need to be cross disciplined from the get go

[jay-anderson]

https://github.com/ibab/tensorflow-wavenet - looks like they're starting to show up.