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 $\boldsymbol \theta$.

Gradient Descent Algorithm in Machine Learning - Analytics Vidhya

Visualizing the vanishing gradient problem

Gradient Descent and its Types - Analytics Vidhya

Gradient Descent With Momentum from Scratch

Gradient Descent Visualization - File Exchange - MATLAB Central

How to implement a gradient descent in Python to find a local minimum ? - GeeksforGeeks

What is Gradient Descent? Gradient Descent in Machine Learning

What is meant by gradient descent in laymen terms? - Quora

ZO-AdaMM: Derivative-free optimization for black-box problems - MIT-IBM Watson AI Lab

The Gradient: A Visual Descent