<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Gradient on felladog</title><link>https://utsavdarlami.github.io/blogs/tags/gradient/</link><description>Recent content in Gradient on felladog</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Tue, 13 Jul 2021 20:13:51 +0545</lastBuildDate><atom:link href="https://utsavdarlami.github.io/blogs/tags/gradient/index.xml" rel="self" type="application/rss+xml"/><item><title>gradient</title><link>https://utsavdarlami.github.io/blogs/notes/2021-05-30--08-53-22z--gradient/</link><pubDate>Sun, 30 May 2021 14:38:00 +0545</pubDate><guid>https://utsavdarlami.github.io/blogs/notes/2021-05-30--08-53-22z--gradient/</guid><description>References : Done Reading: Sebastian Raschka Gradient Descent Lecture Slide To Read: https://www.wikiwand.com/en/Gradient Questions : In vector calculus, the gradient is a multi-variable generalization of the derivative. Whereas the ordinary derivative of a function of a single variable is a scalar-valued function, the gradient of a function of several variables is a vector-valued function
Simply, derivative of multivariable function
The gradient (or gradient vector field) of a scalar function f(x1, x2, x3, …, xn) is denoted ∇f or ∇→f where ∇ (nabla) denotes the vector differential operator, del.</description></item></channel></rss>