Learn the fundamental concepts and step-by-step process of Gradient Descent, a crucial optimization technique in Machine Learning, through this comprehensive 24-minute video. Explore the main ideas behind Gradient Descent, its application in optimizing single and multiple variables, and its importance in various machine learning methods. Discover how Gradient Descent works to fit models to training datasets, and gain insights into Loss Functions, the Gradient Descent algorithm, and Stochastic Gradient Descent. Ideal for those already familiar with Least Squares and Linear Regression, this tutorial provides a deep dive into the workhorse behind most Machine Learning techniques.
Overview
Syllabus
Awesome song and introduction
Main ideas behind Gradient Descent
Gradient Descent optimization of a single variable, part 1
An important note about why we use Gradient Descent
Gradient Descent optimization of a single variable, part 2
Review of concepts covered so far
Gradient Descent optimization of two or more variables
A note about Loss Functions
Gradient Descent algorithm
Stochastic Gradient Descent
Taught by
StatQuest with Josh Starmer
