Lecture 8 Part 2: Automatic Differentiation on Computational Graphs

Подробнее о видео

MIT 18.S096 Matrix Calculus For Machine Learning And Beyond, IAP 2023
Instructors: Alan Edelman, Steven G. Johnson

View the complete course:
YouTube Playlist:

Description: Complicated computational processes can be expressed as “graphs” of computational steps that flow from inputs to outputs. Forward/reverse-mode automatic differentiation (AD) traverse in opposite directions, giving very different algorithms.

License: Creative Commons BY-NC-SA
More information at
More courses at
Support OCW at

We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at .