{"id":6940,"date":"2012-01-16T00:55:00","date_gmt":"2012-01-15T22:55:00","guid":{"rendered":"http:\/\/hgpu.org\/?p=6940"},"modified":"2012-01-16T00:55:00","modified_gmt":"2012-01-15T22:55:00","slug":"theano-deep-learning-on-gpus-with-python","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6940","title":{"rendered":"Theano: Deep Learning on GPUs with Python"},"content":{"rendered":"<p>In this paper, we present Theano, a framework in the Python programming language for defining, optimizing and evaluating expressions involving high-level operations on tensors. Theano offers most of NumPy&#8217;s functionality, but adds automatic symbolic differentiation, GPU support, and faster expression evaluation. Theano is a general mathematical tool, but it was developed with the goal of facilitating research in deep learning. The Deep Learning Tutorials introduce recent advances in deep learning, and showcase how Theano makes such algorithms compact, elegant, and fast.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we present Theano, a framework in the Python programming language for defining, optimizing and evaluating expressions involving high-level operations on tensors. Theano offers most of NumPy&#8217;s functionality, but adds automatic symbolic differentiation, GPU support, and faster expression evaluation. Theano is a general mathematical tool, but it was developed with the goal of [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,89,3],"tags":[1787,955,1782,14,20,251,176,513],"class_list":["post-6940","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-compilers","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-285","tag-package","tag-python"],"views":5635,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6940","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=6940"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6940\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6940"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6940"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6940"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}