Even though Stochastic Gradient Descent sounds fancy, it is just a simple addition to "regular" Gradient Descent. This video sets up the problem that Stochastic Gradient Descent solves and then shows how it does it. Along the way, we discuss situations where Stochastic Gradient Descent is most useful, and some cool features that aren't that obvious.

NOTE: There is a small typo at 9:03. The values for the intercept and slope should be the most recent estimates, 0.86 and 0.68, instead of the original random values, 0 and 1.

NOTE: This StatQuest assumes you already understand "regular" Gradient Descent. If not, check out the 'Quest:    • Gradient Descent, Step-by-Step  

When I was researching Stochastic Gradient Descent, I found a ton of cool websites that provided lots of details. Here are some of my favorites:

Sebastian Ruder has a nice write-up: http://ruder.io/optimizing-gradient-d...

...as the Usupervised Feature Learning and Deep Learning Tutorial: http://deeplearning.stanford.edu/tuto...

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Corrections:
9:03. The values for the intercept and slope should be the most recent estimates, 0.86 and 0.68, instead of the original random values, 0 and 1.
9:33 the slope should be 0.7.

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