Source: tiny-dnn Section: science Priority: optional Maintainer: Debian Deep Learning Team Uploaders: Andrius Merkys , Build-Depends: cmake, debhelper-compat (= 13), doxygen, graphviz, libcereal-dev, libgemmlowp-dev, libgmock-dev , libgtest-dev , libstb-dev, libtbb-dev, Standards-Version: 4.6.2 Homepage: https://github.com/tiny-dnn/tiny-dnn Vcs-Browser: https://salsa.debian.org/deeplearning-team/tiny-dnn Vcs-Git: https://salsa.debian.org/deeplearning-team/tiny-dnn.git Rules-Requires-Root: no Package: tiny-dnn Architecture: all Multi-Arch: foreign Depends: ${misc:Depends}, Suggests: tiny-dnn-doc, Description: header only deep learning framework in C++ tiny-dnn is a C++ implementation of deep learning. It is suitable for deep learning on limited computational resource, embedded systems and IoT devices. . Features: . * Reasonably fast, without GPU; * Portable & header-only; * Easy to integrate with real applications; * Simply implemented. Package: tiny-dnn-doc Architecture: all Multi-Arch: foreign Section: doc Depends: ${misc:Depends}, Description: header only deep learning framework in C++ -- documentation tiny-dnn is a C++ implementation of deep learning. It is suitable for deep learning on limited computational resource, embedded systems and IoT devices. . Features: . * Reasonably fast, without GPU; * Portable & header-only; * Easy to integrate with real applications; * Simply implemented. . This package contains the documentation.