{"id":15465,"date":"2016-02-16T00:21:36","date_gmt":"2016-02-15T22:21:36","guid":{"rendered":"http:\/\/hgpu.org\/?p=15465"},"modified":"2016-02-16T00:21:36","modified_gmt":"2016-02-15T22:21:36","slug":"caffelink-mathematica-binding-for-caffe-deep-learning-framework","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=15465","title":{"rendered":"CaffeLink: Mathematica binding for Caffe Deep Learning Framework"},"content":{"rendered":"<p>In this paper we present CaffeLink an open-source library for Mathematica which is a binding of a well-established Caffe deep learning framework. Caffe is a highly-optimized CUDA accelerated library with focus on convolutional neural networks written in C++ with Python and Matlab bindings. CaffeLink is based upon Mathematica&#8217;s LibraryLink. It makes accessible most features of Caffe directly from Mathematica environment which includes work with datasets, building networks, training them as well as evaluating them. Here we present an overview of the CaffeLink library with examples on MNIST and ImageNet datasets.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper we present CaffeLink an open-source library for Mathematica which is a binding of a well-established Caffe deep learning framework. Caffe is a highly-optimized CUDA accelerated library with focus on convolutional neural networks written in C++ with Python and Matlab bindings. CaffeLink is based upon Mathematica&#8217;s LibraryLink. It makes accessible most features of [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,3],"tags":[1777,1782,14,1673,1025,34,20,176],"class_list":["post-15465","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-caffe","tag-computer-science","tag-cuda","tag-deep-learning","tag-machine-learning","tag-neural-networks","tag-nvidia","tag-package"],"views":2479,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15465","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=15465"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15465\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=15465"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=15465"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=15465"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}