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Dec 11, 2018 at 23:39 answer added n1k31t4 timeline score: 4
Nov 19, 2018 at 11:51 comment added Marco Fumagalli I'd add CNTK , presented in jun 2017 by Microsoft , Python API stable , with performance competiting with tensorflow minimaxir.com/2017/06/keras-cntk
Apr 11, 2018 at 19:28 answer added Federico Caccia timeline score: 3
Oct 13, 2017 at 11:06 comment added j b Please define your criteria for "best"
Jul 17, 2017 at 23:33 comment added gerrit Cross-site duplicate: scicomp.stackexchange.com/q/5110/3212
May 29, 2017 at 17:43 history edited Blaszard CC BY-SA 3.0
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Jan 27, 2017 at 18:46 answer added Franck Dernoncourt timeline score: 4
Dec 12, 2016 at 15:58 history made wiki Post Made Community Wiki by Sean Owen
Dec 12, 2016 at 15:20 review Close votes
Dec 12, 2016 at 15:58
Dec 11, 2016 at 21:03 answer added itdxer timeline score: 2
Oct 26, 2016 at 20:40 answer added Franck Dernoncourt timeline score: 3
Apr 28, 2016 at 6:27 history protected CommunityBot
Apr 1, 2016 at 2:13 review Close votes
Apr 1, 2016 at 15:22
Feb 9, 2016 at 19:29 answer added scttl timeline score: 1
Jan 1, 2016 at 9:19 answer added hoaphumanoid timeline score: 5
Dec 8, 2015 at 17:59 comment added Ogaday I can't vouch for it, but Brainstorm seems to be the spiritual successor to PyBrain and looks promising.
Dec 7, 2015 at 1:10 answer added Franck Dernoncourt timeline score: 5
Nov 26, 2015 at 17:49 review Close votes
Nov 27, 2015 at 13:53
Nov 19, 2015 at 4:16 answer added Franck Dernoncourt timeline score: 7
Nov 10, 2015 at 18:28 answer added srobinson timeline score: 6
Nov 10, 2015 at 1:00 answer added Franck Dernoncourt timeline score: 19
Jul 13, 2015 at 16:11 comment added Emre So use multiple GPUs...nobody uses CPUs for serious work in neural networks. If you can get Google-level performance out of a good GPU or two, just what are you going to do with a thousand CPUs?
Jul 13, 2015 at 15:25 comment added Neil Slater @Emre: Scalable is different to high performance. It typically means you can solve larger problems by adding more resources of the same type you have already. Scalability still wins out, when you have 100 machines available, even if your software is 20 times slower on each of them . . . (although I'd rather pay the price for 5 machines and have benefits of both GPU and multi-machine scale).
Jul 11, 2015 at 6:51 comment added Emre h2o doesn't even use the GPU yet so it's hardly scalable.
Jul 9, 2015 at 21:20 comment added 0xF Does any of this packages scale like h2o deep learning? As far as I know lasagne doesn't.Theano does support GPU so as any library basing on it,but does any of them support mapreduce or spark.
Jul 9, 2015 at 8:37 comment added Neil Slater There is also Keras - github.com/fchollet/keras - which is relatively recent. The problems with tracking "best" by any measure, and keeping the Q&A valid over time is why this sort of question is usually off topic in other Stack Exchange networks.
Jul 9, 2015 at 0:15 answer added Def_Os timeline score: 9
Jul 7, 2015 at 12:20 comment added Rafael_Espericueta And now there's a new contender - Scikit Neuralnetwork: Has anyone had experience with this yet? How does it compare with Pylearn2 or Theano?
Mar 25, 2015 at 19:45 answer added Martin Thoma timeline score: 41
Sep 17, 2014 at 11:42 comment added user70747 If you want to only use Restricted Boltzmann Machine, you can stick with scikit-learn as well.
Jul 21, 2014 at 15:35 answer added royalstream timeline score: 9
Jul 17, 2014 at 18:25 answer added user2556 timeline score: 6
Jul 9, 2014 at 0:05 vote accept marcodena
Jul 8, 2014 at 20:14 comment added Air See also stackoverflow.com/q/2276933/2359271
Jul 8, 2014 at 12:38 review Close votes
Jul 9, 2014 at 0:08
Jul 8, 2014 at 10:36 answer added jnovacho timeline score: 23
Jul 7, 2014 at 19:55 answer added Madison May timeline score: 132
Jul 7, 2014 at 19:17 history asked marcodena CC BY-SA 3.0