Deep learning has recently gained a lot of attention on account of its incredible success in many complex data-driven applications, such as image classification. However, deep learning is quite user-hostile and is thus difficult to apply. For example, it is tricky and slow to train a large model which may consume a lot of memory. This thesis introduces our investigations and approaches towards these challenges. First, we have conducted a comprehensive analysis of optimization techniques for deep learning systems, including stand-alone and distributed training. Second, we have designed and developed a distributed deep learning system, named SINGA, which tackles the usability problem and realizes optimization techniques for distributed training. SINGA provides a flexible system architecture for running different distributed training frameworks. Last, we have proposed deep learning based methods for effective multi-modal retrieval on top of SINGA, which outperform state-of-the-art approaches.<\/p>\n","protected":false},"excerpt":{"rendered":"
Deep learning has recently gained a lot of attention on account of its incredible success in many complex data-driven applications, such as image classification. However, deep learning is quite user-hostile and is thus difficult to apply. For example, it is tricky and slow to train a large model which may consume a lot of memory. […]<\/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":[1782,14,1673,20,1779,1767,176,513,390],"class_list":["post-17197","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-deep-learning","tag-nvidia","tag-nvidia-geforce-gtx-970","tag-nvidia-geforce-gtx-titan-x","tag-package","tag-python","tag-thesis"],"views":2326,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17197","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=17197"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17197\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=17197"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=17197"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=17197"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}