Visualizing the gradient descent method

In the gradient descent method of optimization, a hypothesis function, $h_\boldsymbol{\theta}(x)$, is fitted to a data set, $(x^{(i)}, y^{(i)})$ ($i=1,2,\cdots,m$) by minimizing an associated cost function, $J(\boldsymbol{\theta})$ in terms of the parameters $\boldsymbol\theta = \theta_0, \theta_1, \cdots$. The cost function describes how closely the hypothesis fits the data for a given choice of $\boldsymbol \theta$.

Why Visualize Gradient Descent Optimization Algorithms ?, by ASHISH RANA

A Data Scientist's Guide to Gradient Descent and Backpropagation Algorithms

How to visualize Gradient Descent using Contour plot in Python

Gradient Descent Optimization – Towards AI

Orange Data Mining - Visualizing Gradient Descent

The Gradient: A Visual Descent

Simplistic Visualization on How Gradient Descent works

Visualizing Gradient Descent in 3D - Part 1 2022 - fast.ai Course Forums

Gradient Descent With AdaGrad From Scratch

Visualize the gradient descent of a cost function with its level circles -Python, by Joséphine Picot, Analytics Vidhya