Gradient Descent is the workhorse behind most of Machine Learning. When you fit a machine learning method to a training dataset, you’re probably using Gradient Descent. It can optimize parameters in a wide variety of settings. Since it’s so fundamental to Machine Learning, I decided to make a “step-by-step” video that shows you exactly how it works.
NOTE: This video assumes you are already familiar with Least Squares and Linear Regression. If not, here’s the link to the Quest: https://youtu.be/PaFPbb66DxQ
For a complete index of all the StatQuest videos, check out:
Sources:
There are a ton of websites that describe the math behind Gradient Descent. One of my favorite is the wikipedia article: https://en.wikipedia.org/wiki/Gradient_descent
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Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
Tweets by joshuastarmer
0:00 Awesome song and introduction
1:25 Main ideas behind Gradient Descent
5:38 Gradient Descent optimization of a single variable, part 1
9:08 An important note about why we use Gradient Descent
9:40 Gradient Descent optimization of a single variable, part 2
14:48 Review of concepts covered so far
15:48 Gradient Descent optimization of two (or more) variables
21:55 A note about Loss Functions
22:13 Gradient Descent algorithm
23:06 Stochastic Gradient Descent
#statquest #gradient #descent #ML
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