Hey Mike, A friend asked me if I know of any good (usable) deep learning libraries for Clojure. I remembered you had some earlier neural networks library that was at least OK for experimenting, but seems abandoned for your current work in a similar domain. A bit of digging lead me to this post.
I understand that this library may not be completely ready yet, but I wandered wheter now you were able to give a better estimation of where it stands in comparison with other DL offerings, like what deeplearning4j guys are doing, or even with the established non-Java libraries such as Theano, Torch, Caffe, and TensorFlow. What is the chance of you releasing it even if it is not 100% ready? I get the reluctance to commit to a certain API, but I don't think everyone will rush to commit their code to the API you release anyway, and the open development will certainly help both the (potential) users and your team (by returning free testing & feedback). On Tuesday, May 31, 2016 at 7:17:35 AM UTC+2, Mikera wrote: > > 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? >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> -- 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.
