Enable javascript in your browser for better experience. Need to know to enable it? Go here.
已发布 : Mar 29, 2017
不在本期内容中
这一条目不在当前版本的技术雷达中。如果它出现在最近几期中,那么它很有可能仍然具有相关参考价值。如果这一条目出现在更早的雷达中,那么它很有可能已经不再具有相关性,我们的评估将不再适用于当下。很遗憾我们没有足够的带宽来持续评估以往的雷达内容。 了解更多
Mar 2017
评估 ? 在了解它将对你的企业产生什么影响的前提下值得探索

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.

Radar

下载第25期技术雷达

English | Español | Português | 中文

Radar

获取最新技术洞见

 

立即订阅

查看存档并阅读往期内容