Machine Learning - Stochastic Gradient Descent, Clearly Explained!!!

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: https://youtu.be/sDv4f4s2SB8 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-descent/ ...as the Usupervised Feature Learning and Deep Learning Tutorial: http://deeplearning.stanford.edu/tutorial/supervised/OptimizationStochasticGradientDescent/ For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider... Patreon: https://www.patreon.com/statquest ...or... YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join ...buying one of my books, a study guide, a t-shirt or hoodie, or a song from the StatQuest store... https://statquest.org/statquest-store/ ...or just donating to StatQuest! https://www.paypal.me/statquest Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: https://twitter.com/joshuastarmer 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. #statquest #sgd