Lecture 17 – Gradient Descent
Presented by Anthony D. Joseph
Content by Josh Hug, Joseph Gonzalez
- slides
- video playlist
- code
- code HTML
- (optional) Loss Game
The Quick Check for this lecture is due Monday, November 2nd at 11:59PM. A random one of the following Google Forms will give you an alphanumeric code once you submit; you should take this code and enter it into the “Lecture 17” question in the “Quick Check Codes” assignment on Gradescope to get credit for submitting this Quick Check.
| Video | Quick Check | |
|---|---|---|
| 17.1 Gradient descent in one dimension. Convexity. |
17.1 | |
| 17.2 Various methods of optimizing loss functions in one dimension. |
17.2 | |
| 17.3 Gradient descent in multiple dimensions. Interpretation of gradients. |
17.3 | |
| 17.4 Stochastic gradient descent (SGD). Comparison between gradient descent and SGD. |
17.4 |