Difference between revisions of "Caffe / Caffe2"
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| + | Deep-learning framework Caffe is “made with expression, speed, and modularity in mind.” Originally developed in 2013 for machine vision projects, Caffe has since expanded to include other applications, such as speech and multimedia. Speed is a major priority, so Caffe has been written entirely in C++, with CUDA acceleration support, although it can switch between CPU and GPU processing as needed. The distribution includes a set of free and open source reference models for common classification jobs, with other models created and donated by the Caffe user community. A new iteration of Caffe backed by Facebook, called Caffe2, is currently under development for a 1.0 release. Its goals are to make it easier to perform distributed training and deploy to mobile devices, to provide support for new kinds of hardware like FPGAs, and to make use of cutting-edge features like 16-bit floating-point training. [http://www.infoworld.com/article/3026262/machine-learning/13-frameworks-for-mastering-machine-learning.html 13 frameworks for mastering machine learning | Serdar Yegulalp] | ||
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Revision as of 21:59, 22 February 2019
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Deep-learning framework Caffe is “made with expression, speed, and modularity in mind.” Originally developed in 2013 for machine vision projects, Caffe has since expanded to include other applications, such as speech and multimedia. Speed is a major priority, so Caffe has been written entirely in C++, with CUDA acceleration support, although it can switch between CPU and GPU processing as needed. The distribution includes a set of free and open source reference models for common classification jobs, with other models created and donated by the Caffe user community. A new iteration of Caffe backed by Facebook, called Caffe2, is currently under development for a 1.0 release. Its goals are to make it easier to perform distributed training and deploy to mobile devices, to provide support for new kinds of hardware like FPGAs, and to make use of cutting-edge features like 16-bit floating-point training. 13 frameworks for mastering machine learning | Serdar Yegulalp
