Very cool! Does it work with distributions.jl so we can write probabilistic 
models?

On Tuesday, September 20, 2016 at 2:31:31 PM UTC-4, Deniz Yuret wrote:
>
> It has been a year and a half since I wrote the first version of this post 
> <https://groups.google.com/d/topic/julia-users/rSgtUpcYPbE/discussion> 
> and it is time for an update, just in time with the Julia 0.5.0 release!  
>
> Knet <https://github.com/denizyuret/Knet.jl> (pronounced “kay-net”) is 
> the Koç University deep learning framework implemented in Julia. The latest 
> version (0.8.0) is finally registered as an official Julia package.  Unlike 
> gradient generating compilers like Theano and TensorFlow which force users 
> into a restricted mini-language, Knet allows the definition and training of 
> machine learning models using the full power and expressivity of Julia. 
>  Models are defined by describing only the forward calculation in plain 
> Julia using helper functions, loops, conditionals, recursion, closures, 
> tuples and dictionaries, array indexing and concatenation and almost 
> everything else Julia offers. High performance is achieved by combining 
> automatic differentiation of most of Julia with efficient GPU kernels and 
> memory management. The computations can be performed on the GPU by simply 
> using KnetArray instead of Array for parameters and data. To find out more 
> and see some examples check out the README 
> <https://github.com/denizyuret/Knet.jl/blob/master/README.rst>.
>
> best,
> deniz
>
>

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