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Knet.jl is the Koç University deep-learning framework implemented in Julia by Deniz Yuret and collaborators. Unlike gradient-generating compilers such as 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 expressiveness of Julia. Knet uses dynamic computational graphs generated at runtime for the automatic differentiation of almost any Julia code. We really like the support of GPU operations through the KnetArray type, and in case you don't have access to a GPU machine, the team behind Knet also maintains a preconfigured Amazon Machine Image (AMI) so you can evaluate it in the cloud.

History for Knet.jl

Mar 2017
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Knet.jl is the Koç University deep-learning framework implemented in Julia by Deniz Yuret and collaborators. Unlike gradient-generating compilers such as 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 expressiveness of Julia. Knet uses dynamic computational graphs generated at runtime for the automatic differentiation of almost any Julia code. We really like the support of GPU operations through the KnetArray type, and in case you don't have access to a GPU machine, the team behind Knet also maintains a preconfigured Amazon Machine Image (AMI) so you can evaluate it in the cloud.