{"id":5170,"date":"2019-10-15T17:58:59","date_gmt":"2019-10-15T16:58:59","guid":{"rendered":"https:\/\/www.blopig.com\/blog\/?p=5170"},"modified":"2019-10-15T18:14:51","modified_gmt":"2019-10-15T17:14:51","slug":"a-gentle-introduction-to-the-gpyopt-module","status":"publish","type":"post","link":"https:\/\/www.blopig.com\/blog\/2019\/10\/a-gentle-introduction-to-the-gpyopt-module\/","title":{"rendered":"A Gentle Introduction to the GPyOpt Module"},"content":{"rendered":"\n<p>Manually tuning hyperparameters in a neural network is slow and boring. Using Bayesian Optimisation to do it for you is slightly less slower and you can go do other things whilst it&#8217;s running. Susan recently highlighted some of the resources available to get to grips with GPyOpt. Below is a copy of a Jupyter Notebook where we walk through a couple of simple examples and hopefully shed a little bit of light on how the algorithm works.<\/p>\n\n\n\n<!--more-->\n\n\n\n<!-- iframe plugin v.6.0 wordpress.org\/plugins\/iframe\/ -->\n<iframe loading=\"lazy\" src=\"https:\/\/www.blopig.com\/blog\/wp-content\/uploads\/2019\/10\/GPyOpt-Tutorial1.html\" width=\"100%\" height=\"500\" scrolling=\"yes\" class=\"iframe-class\" frameborder=\"0\"><\/iframe>\n\n","protected":false},"excerpt":{"rendered":"<p>Manually tuning hyperparameters in a neural network is slow and boring. Using Bayesian Optimisation to do it for you is slightly less slower and you can go do other things whilst it&#8217;s running. Susan recently highlighted some of the resources available to get to grips with GPyOpt. Below is a copy of a Jupyter Notebook [&hellip;]<\/p>\n","protected":false},"author":65,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"nf_dc_page":"","wikipediapreview_detectlinks":true,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"ngg_post_thumbnail":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[29,189,258,227],"tags":[],"ppma_author":[545],"class_list":["post-5170","post","type-post","status-publish","format-standard","hentry","category-code","category-machine-learning","category-optimization","category-python-code"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"authors":[{"term_id":545,"user_id":65,"is_guest":0,"slug":"tom","display_name":"Thomas Hadfield","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/c77f10ae0d9e0e114df272f52ae30c232859886b12e9c0fcf3987102ee11d81c?s=96&d=mm&r=g","0":null,"1":"","2":"","3":"","4":"","5":"","6":"","7":"","8":""}],"_links":{"self":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/5170","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/users\/65"}],"replies":[{"embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/comments?post=5170"}],"version-history":[{"count":3,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/5170\/revisions"}],"predecessor-version":[{"id":5178,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/5170\/revisions\/5178"}],"wp:attachment":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/media?parent=5170"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/categories?post=5170"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/tags?post=5170"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=5170"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}