I've been working with a number of collaborators on a deep learning library 
for Clojure. 

Some key features:
- An abstract API for key machine learning functionality
- Ability to declare graphs / stacks of operations (somewhat analogous to 
tensorflow)
- Support for multiple underlying implementations (ClojureScript, JVM, CPU, 
GPU)
- Integration with core.matrix for N-dimensional data processing

We intend to release as open source. We haven't released yet because we 
want to get the API right first but it is looking very promising.

On Tuesday, 31 May 2016 02:34:41 UTC+8, 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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