Fall 2026 · Lecture 18 · November 4, 2026
RNN, LSTM
Modeling sequences with shared state and recurrent computation.
Learning goals
- Express recurrent state updates.
- Explain backpropagation through time.
- Recognize vanishing gradients and gated alternatives.
Materials
Slides, code, examples, and references will be added after class.
Lecture notes
An unrolled network visual, sequence example, and limitations will be added here.
Lecture extensions
Course materials and optional topics
Four independently reviewed sections
Assignment pending
Develop the assigned part of the lecture’s core course materials.
The course team will assign this section through a GitHub issue.
Assignment pending
Develop the assigned part of the lecture’s core course materials.
The course team will assign this section through a GitHub issue.
Assignment pending
Develop an optional extension, application, or related topic assigned for this lecture.
The course team will assign this section through a GitHub issue.
Assignment pending
Develop an optional extension, application, or related topic assigned for this lecture.
The course team will assign this section through a GitHub issue.