{"id":506,"date":"2020-02-25T07:02:25","date_gmt":"2020-02-25T07:02:25","guid":{"rendered":"http:\/\/smartdemowp.com\/detox\/?p=506"},"modified":"2023-01-31T21:15:07","modified_gmt":"2023-01-31T20:15:07","slug":"additional-monster-fo-ther-gmae-sota-yhusy","status":"publish","type":"post","link":"https:\/\/dataminds.fr\/index.php\/2020\/02\/25\/additional-monster-fo-ther-gmae-sota-yhusy\/","title":{"rendered":"Higher-Order Derivatives in Machine Learning in ML"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"506\" class=\"elementor elementor-506\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8f8abc8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8f8abc8\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4f24c72\" data-id=\"4f24c72\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b02960e elementor-widget elementor-widget-text-editor\" data-id=\"b02960e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"text\"><p><span style=\"font-weight: 400;\">Higher-order derivatives have the ability to capture information about a function that first-order derivatives alone cannot.<\/span><\/p><p><span style=\"font-weight: 400;\">First-order derivatives can capture critical information like the rate of change, but they can&#8217;t tell the difference between local minima and maxima with the same rate of change.<\/span><\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e0bc3d2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e0bc3d2\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-395b363\" data-id=\"395b363\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e162447 elementor-widget elementor-widget-text-editor\" data-id=\"e162447\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Lorem ipsum dolor sit amet consectetur adipisicing elit sed do eiusmod tempor incididunt labore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris aliquip commodo consequat.<\/p><p><span style=\"font-weight: 400;\">Several optimization techniques use the usage of higher-order derivatives to overcome this limitation, such as Newton&#8217;s approach, which uses second-order derivatives to attain the local minimum of an optimization function.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-243c1df\" data-id=\"243c1df\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6b2f935 elementor-widget elementor-widget-image\" data-id=\"6b2f935\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"370\" height=\"420\" src=\"https:\/\/dataminds.fr\/wp-content\/uploads\/2020\/06\/project-11.jpg\" class=\"attachment-full size-full wp-image-78\" alt=\"\" srcset=\"https:\/\/dataminds.fr\/wp-content\/uploads\/2020\/06\/project-11.jpg 370w, https:\/\/dataminds.fr\/wp-content\/uploads\/2020\/06\/project-11-264x300.jpg 264w\" sizes=\"(max-width: 370px) 100vw, 370px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8bc2041 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8bc2041\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ca68775\" data-id=\"ca68775\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-747b206 elementor-widget elementor-widget-text-editor\" data-id=\"747b206\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The second-order derivative is the most commonly employed derivative in machine learning. We already discussed how the second derivative can offer us information that the first derivative alone cannot.<\/span><\/p><p>\u00a0<\/p><p><span style=\"font-weight: 400;\">It can inform us whether a critical point is a local minimum or maximum (depending on whether the second derivative is higher or smaller than zero), while the first derivative would otherwise be zero in both circumstances.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Lorem ipsum dolor sit amet consectetur adipisicing elit sed do eiusmod tempor incididunt labore.<\/p>\n","protected":false},"author":1,"featured_media":507,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8],"tags":[13],"class_list":["post-506","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-science","tag-saas"],"_links":{"self":[{"href":"https:\/\/dataminds.fr\/index.php\/wp-json\/wp\/v2\/posts\/506","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dataminds.fr\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dataminds.fr\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dataminds.fr\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/dataminds.fr\/index.php\/wp-json\/wp\/v2\/comments?post=506"}],"version-history":[{"count":1,"href":"https:\/\/dataminds.fr\/index.php\/wp-json\/wp\/v2\/posts\/506\/revisions"}],"predecessor-version":[{"id":1045,"href":"https:\/\/dataminds.fr\/index.php\/wp-json\/wp\/v2\/posts\/506\/revisions\/1045"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dataminds.fr\/index.php\/wp-json\/wp\/v2\/media\/507"}],"wp:attachment":[{"href":"https:\/\/dataminds.fr\/index.php\/wp-json\/wp\/v2\/media?parent=506"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dataminds.fr\/index.php\/wp-json\/wp\/v2\/categories?post=506"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dataminds.fr\/index.php\/wp-json\/wp\/v2\/tags?post=506"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}