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? -- You received this message because you are subscribed to the Google Groups "Clojure" group. To post to this group, send email to clojure@googlegroups.com Note that posts from new members are moderated - please be patient with your first post. To unsubscribe from this group, send email to clojure+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/clojure?hl=en --- You received this message because you are subscribed to the Google Groups "Clojure" group. To unsubscribe from this group and stop receiving emails from it, send an email to clojure+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.
