{"id":9191,"date":"2025-12-18T07:02:23","date_gmt":"2025-12-18T07:02:23","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/12\/18\/the-machine-learning-advent-calendar-day-17-neural-network-regressor-in-excel\/"},"modified":"2025-12-18T07:02:23","modified_gmt":"2025-12-18T07:02:23","slug":"the-machine-learning-advent-calendar-day-17-neural-network-regressor-in-excel","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/12\/18\/the-machine-learning-advent-calendar-day-17-neural-network-regressor-in-excel\/","title":{"rendered":"The Machine Learning \u201cAdvent Calendar\u201d Day 17: Neural Network Regressor in Excel"},"content":{"rendered":"<p>    The Machine Learning \u201cAdvent Calendar\u201d Day 17: Neural Network Regressor 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>Neural networks often feel like black boxes. In this article, we build a neural network regressor from scratch using only Excel formulas. By making every step explicit, from forward propagation to backpropagation, we show how a neural network learns to approximate non-linear functions with just a handful of parameters.<\/p>\n<p>The post <a href=\"https:\/\/towardsdatascience.com\/the-machine-learning-advent-calendar-day-17-neural-network-regressor-in-excel\/\">The Machine Learning \u201cAdvent Calendar\u201d Day 17: Neural Network Regressor 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-day-17-neural-network-regressor-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 Day 17: Neural Network Regressor in Excel Neural networks often feel like black boxes. In this article, we build a neural network regressor from scratch using only Excel formulas. By making every step explicit, from forward propagation to backpropagation, we show how a neural network learns to approximate non-linear functions [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,69,2583,83,245,70,1780],"tags":[132,118,4448],"class_list":["post-9191","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-artificial-intelligence","category-backpropagation","category-data-science","category-excel","category-machine-learning","category-neural-network","tag-network","tag-neural","tag-regressor"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/9191"}],"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=9191"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/9191\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=9191"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=9191"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=9191"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}