{"id":25297,"date":"2016-10-24T16:38:54","date_gmt":"2016-10-24T21:38:54","guid":{"rendered":"http:\/\/www.dannyadam.com\/blog\/?p=25297"},"modified":"2019-11-05T16:25:38","modified_gmt":"2019-11-05T21:25:38","slug":"factorization-machines-with-theano","status":"publish","type":"post","link":"https:\/\/www.dannyadam.com\/blog\/2016\/10\/factorization-machines-with-theano\/","title":{"rendered":"Factorization Machines with Theano"},"content":{"rendered":"<p><strong>Update 11\/4\/2019<\/strong>: The github repo was renamed from <em>PyFactorizationMachines<\/em> to <em>pyfms<\/em>.<\/p>\n<p><strong>Update 4\/20\/2017<\/strong>: The library\u00a0is now <a href=\"https:\/\/pypi.python.org\/pypi\/pyfms\">available<\/a>\u00a0on PyPI, the Python Package Index. It can be installed with <em>pip<\/em>.<\/p>\n<pre>$ pip install pyfms<\/pre>\n<p>A<em> Factorization Machine<\/em>\u00a0(FM) is a predictive model that can be used for regression and classification (Rendle 2010). FMs efficiently incorporate pairwise interactions by using factorized parameters.<\/p>\n<p><a href=\"https:\/\/github.com\/dstein64\/pyfms\">PyFactorizationMachines<\/a> is a Theano-based Python implementation of factorization machines. documentation, see <a href=\"https:\/\/github.com\/dstein64\/pyfms\/blob\/master\/documentation.md\">documentation.md<\/a>.<\/p>\n<p>For example usage, see <a href=\"https:\/\/github.com\/dstein64\/pyfms\/blob\/master\/example.py\">example.py<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Update 11\/4\/2019: The github repo was renamed from PyFactorizationMachines to pyfms. Update 4\/20\/2017: The library\u00a0is now available\u00a0on PyPI, the Python Package Index. It can be installed with pip. $ pip install pyfms A Factorization Machine\u00a0(FM) is a predictive model that can be used for regression and classification (Rendle 2010). FMs efficiently incorporate pairwise interactions by [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":true,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_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":[1],"tags":[49,46,36,44],"class_list":["post-25297","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-factorization-machines","tag-machine-learning","tag-python","tag-theano"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p1sCC6-6A1","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.dannyadam.com\/blog\/wp-json\/wp\/v2\/posts\/25297","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dannyadam.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.dannyadam.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.dannyadam.com\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dannyadam.com\/blog\/wp-json\/wp\/v2\/comments?post=25297"}],"version-history":[{"count":12,"href":"https:\/\/www.dannyadam.com\/blog\/wp-json\/wp\/v2\/posts\/25297\/revisions"}],"predecessor-version":[{"id":26141,"href":"https:\/\/www.dannyadam.com\/blog\/wp-json\/wp\/v2\/posts\/25297\/revisions\/26141"}],"wp:attachment":[{"href":"https:\/\/www.dannyadam.com\/blog\/wp-json\/wp\/v2\/media?parent=25297"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dannyadam.com\/blog\/wp-json\/wp\/v2\/categories?post=25297"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dannyadam.com\/blog\/wp-json\/wp\/v2\/tags?post=25297"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}