Small addition to this post:

There is a tiny library (toy project) of ddosic, who build a neural network 
with neanderthal. It might be interesting as a benchmark of what speed 
neanderthal can reach (although it might not currently be a good reflection 
of neanderthal), versus larger packages with more overhead.

https://github.com/ddosic/affe

On Monday, May 30, 2016 at 8:34:41 PM UTC+2, kovasb wrote:
>
> Anyone seriously working on deep learning with Clojure?
>
> I'm working with Torch at the day job, and have done work integrating 
> Tensorflow into Clojure, so I'm fairly familiar with the challenges of what 
> needs to be done. A bit too much to bite off on my own in my spare time. 
>
> So is anyone out there familiar enough with these tools to have a sensible 
> conversation of what could be done in Clojure?
>
> The main question on my mind is: what level of abstraction would be useful?
>
> All the existing tools have several layers of abstraction. In Tensorflow, 
> at the bottom theres the DAG of operations, and above that a high-level 
> library of python constructs to build the DAG (and now of course libraries 
> going higher still). In Torch, its more complicated: there's the excellent 
> tensor library at the bottom; the NN modules that are widely used; and 
> various non-orthogonal libraries and modules stack on top of those. 
>
> One could try to integrate at the bottom layer, and then re-invent the 
> layers above that in Clojure. Or one could try to integrate at the higher 
> layers, which is more complicated, but gives more leverage from the 
> existing ecosystem. 
>
> Any thoughts?
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