{"version":"1.0","provider_name":"CloudxLab Blog","provider_url":"https:\/\/cloudxlab.com\/blog","title":"Coding Backpropagation and Gradient Descent From Scratch without using any libraries | CloudxLab Blog","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\"><a href=\"https:\/\/cloudxlab.com\/blog\/backpropagation-from-scratch\/\">Coding Backpropagation and Gradient Descent From Scratch without using any libraries<\/a><\/blockquote>\n<script type='text\/javascript'>\n<!--\/\/--><![CDATA[\/\/><!--\n\t\t\/*! 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It is mainly used in training the neural network. And backpropagation is basically gradient descent. What if we tell you that understanding and implementing it is not that hard?\u00a0 Anyone who knows basic Mathematics and has knowledge of the basics of Python Language can &hellip; Continue reading 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%0A"}