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The information in our interactive Radar is currently only available in English. To get information in your native language, please download the PDF here.

ARCHIVED BLIP
Please be aware that we have archived this blip and are no longer actively keeping the information updated. The current edition of the radar only features items that we feel are new or noteworthy.Understand more
ASSESS?

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
Assess?

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.