I think TensorFlow.jl is a great idea. Also their distributed computation framework is also the kind that we want to have in Julia.
I have created JuliaML. Send me email if you want to be part of it, and I will make you an owner. Perhaps we can even move some of the JuliaStats ML projects to JuliaML. -viral On Wednesday, November 11, 2015 at 11:27:21 AM UTC+5:30, Valentin Churavy wrote: > > It fits in the same niche that Mocha.jl and MXNet.jl are filling right > now. MXNet is a ML library that shares many of the same design ideas of > TensorFlow and has great Julia support https://github.com/dmlc/MXNet.jl > > > On Wednesday, 11 November 2015 01:04:00 UTC+9, Randy Zwitch wrote: >> >> For me, the bigger question is how does TensorFlow fit in/fill in gaps in >> currently available Julia libraries? I'm not saying that someone who is >> sufficiently interested shouldn't wrap the library, but it'd be great to >> identify what major gaps remain in ML for Julia and figure out if >> TensorFlow is the right way to proceed. >> >> We're certainly nowhere near the R duplication problem yet, but certainly >> we're already repeating ourselves in many areas. >> >> On Monday, November 9, 2015 at 4:02:36 PM UTC-5, Phil Tomson wrote: >>> >>> Google has released it's deep learning library called TensorFlow as open >>> source code: >>> >>> https://github.com/tensorflow/tensorflow >>> >>> They include Python bindings, Any ideas about how easy/difficult it >>> would be to create Julia bindings? >>> >>> Phil >>> >>
