Lecture 5 Part 2: Forward Automatic Differentiation via Dual Numbers

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MIT 18.S096 Matrix Calculus For Machine Learning And Beyond, IAP 2023 Instructors: Alan Edelman, Steven G. Johnson View the complete course: https://ocw.mit.edu/courses/18-s096-matrix-calculus-for-machine-learning-and-beyond-january-iap-2023/ YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP62EaLLH92E_VCN4izBKK6OE Description: One simple way to automatically differentiate (AD) computer programs (in “forward mode”) is to define a new kind of number that carries both value and derivative information: dual numbers. A few lines of Julia let us build an AD system! License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu Support OCW at http://ow.ly/a1If50zVRlQ 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 https://ocw.mit.edu/comments.

Published 2023-12-01 · Watch on YouTube ↗
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