Where is the D deep learning library?

[jmh530 via Digitalmars-d]

On Monday, 27 June 2016 at 22:17:55 UTC, Martin Nowak wrote:
> On 06/27/2016 08:13 PM, Guillaume Piolat wrote:
>> Not yet, but it could be useful for new types of audio effects 
>> and specific tasks like voiced/unvoiced detection.
>
> There are many simpler solutions for that than using machine 
> learning. Writing a simple neural network with backpropagation 
> is fairly trivial, if you had that in mind to emulate existing 
> audio effects, not sure if it works well though.

I could probably write a simple backpropogation one, but I would 
probably screw something up if I wrote my own convolutional 
neural network.

My suggestion is that anyone interested in deep learning might 
want to break it up into some more manageable projects.

For instance, one of the features of TensorFlow is 
auto-differentiation. This means that you can provide it 
arbitrary functions and it will calculate the gradients for you 
exactly instead of relying on numerical estimates. autodiff 
involves building an AST for a function and then walking it to 
generate the gradient. A D autodiff library would probably be 
easier to write than a comparable one in other languages since it 
could take advantage of all the compile time functionality.

Alternately, TensorFlow also works well with heterogeneous 
systems, so any work that improves D's capabilities with 
OpenCL/CUDA or MPI would be something that might make it easier 
to develop a D Deep Learning library.