Lecture 20 – Gradient Descent
Presented by Anthony D. Joseph
Content by Josh Hug, Joseph Gonzalez
A reminder – the right column of the table below contains Quick Checks. These are not required but suggested to help you check your understanding.
| Video | Quick Check | |
|---|---|---|
| 20.1 Gradient descent in one dimension. Convexity. |
20.1 | |
| 20.2 Various methods of optimizing loss functions in one dimension. |
20.2 | |
| 20.3 Gradient descent in multiple dimensions. Interpretation of gradients. |
20.3 | |
| 20.4 Stochastic gradient descent (SGD). Comparison between gradient descent and SGD. |
20.4 |