{"id":9446,"date":"2026-01-01T07:02:45","date_gmt":"2026-01-01T07:02:45","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2026\/01\/01\/the-machine-learning-advent-calendar-bonus-2-gradient-descent-variants-in-excel\/"},"modified":"2026-01-01T07:02:45","modified_gmt":"2026-01-01T07:02:45","slug":"the-machine-learning-advent-calendar-bonus-2-gradient-descent-variants-in-excel","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2026\/01\/01\/the-machine-learning-advent-calendar-bonus-2-gradient-descent-variants-in-excel\/","title":{"rendered":"The Machine Learning \u201cAdvent Calendar\u201d Bonus 2: Gradient Descent Variants in Excel"},"content":{"rendered":"<p>    The Machine Learning \u201cAdvent Calendar\u201d Bonus 2: Gradient Descent Variants in Excel<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>\n<p>Gradient Descent, Momentum, RMSProp, and Adam all aim for the same minimum. They do not change the destination, only the path. Each method adds a mechanism that fixes a limitation of the previous one, making the movement faster, more stable, or more adaptive. The goal stays the same. The update becomes smarter.<\/p>\n<p>The post <a href=\"https:\/\/towardsdatascience.com\/the-machine-learning-advent-calendar-bonus-2-gradient-descent-variants-in-excel\/\">The Machine Learning \u201cAdvent Calendar\u201d Bonus 2: Gradient Descent Variants in Excel<\/a> appeared first on <a href=\"https:\/\/towardsdatascience.com\/\">Towards Data Science<\/a>.<\/p>\n<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    angela shi<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/towardsdatascience.com\/the-machine-learning-advent-calendar-bonus-2-gradient-descent-variants-in-excel\/\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Machine Learning \u201cAdvent Calendar\u201d Bonus 2: Gradient Descent Variants in Excel Gradient Descent, Momentum, RMSProp, and Adam all aim for the same minimum. They do not change the destination, only the path. Each method adds a mechanism that fixes a limitation of the previous one, making the movement faster, more stable, or more adaptive. [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,69,83,245,1117,70],"tags":[2060,379,341],"class_list":["post-9446","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-artificial-intelligence","category-data-science","category-excel","category-gradient-descent","category-machine-learning","tag-descent","tag-gradient","tag-machine"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/9446"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=9446"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/9446\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=9446"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=9446"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=9446"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}