Uploaded October 2020 | Updated September 2026, 1 week ago
Backpropagation is the method we use to optimize parameters in a Neural Network. The ideas behind backpropagation are quite simple, but there are tons of details. This StatQuest focuses on explaining the main ideas in a way that is easy to understand.
NOTE: This StatQuest assumes that you already know the main ideas behind...
Neural Networks: youtu.be/CqOfi41LfDw
The Chain Rule: youtu.be/wl1myxrtQHQ
Gradient Descent: youtu.be/sDv4f4s2SB8
LAST NOTE: When I was researching this 'Quest, I found this page by Sebastian Raschka to be helpful: sebastianraschka.com/faq/docs/backprop-arbitrary.html
For a complete index of all the StatQuest videos, check out:
statquest.org/video-index
If you'd like to support StatQuest, please consider...
Patreon: patreon.com/statquest
...or...
YouTube Membership: 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...
statquest.org/statquest-store
...or just donating to StatQuest!
paypal.me/statquest
Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
twitter.com/joshuastarmer
0:00 Awesome song and introduction
3:55 Fitting the Neural Network to the data
6:04 The Sum of the Squared Residuals
7:23 Testing different values for a parameter
8:38 Using the Chain Rule to calculate a derivative
13:28 Using Gradient Descent
16:05 Summary
#StatQuest #NeuralNetworks #Backpropagation
Backpropagation is the method we use to optimize parameters in a Neural Network. The ideas behind backpropagation are quite simple, but there are tons of details. This StatQuest focuses on explaining the main ideas in a way that is easy to understand.
NOTE: This StatQuest assumes that you already know the main ideas behind...
Neural Networks: youtu.be/CqOfi41LfDw
The Chain Rule: youtu.be/wl1myxrtQHQ
Gradient Descent: youtu.be/sDv4f4s2SB8
LAST NOTE: When I was researching this 'Quest, I found this page by Sebastian Raschka to be helpful: sebastianraschka.com/faq/docs/backprop-arbitrary.html
For a complete index of all the StatQuest videos, check out:
statquest.org/video-index
If you'd like to support StatQuest, please consider...
Patreon: patreon.com/statquest
...or...
YouTube Membership: 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...
statquest.org/statquest-store
...or just donating to StatQuest!
paypal.me/statquest
Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
twitter.com/joshuastarmer
0:00 Awesome song and introduction
3:55 Fitting the Neural Network to the data
6:04 The Sum of the Squared Residuals
7:23 Testing different values for a parameter
8:38 Using the Chain Rule to calculate a derivative
13:28 Using Gradient Descent
16:05 Summary
#StatQuest #NeuralNetworks #Backpropagation
