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Gradient Descent, Step-by-Step

StatQuest with Josh Starmer via YouTube

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Overview

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This course explains gradient descent step by step through least-squares line fitting, covering derivatives, learning rates, loss functions, parameter updates, and stopping criteria. It extends the method to multiple parameters and introduces stochastic gradient descent; familiarity with least squares and linear regression is assumed.

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

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