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Lecture 5 Part 1: Derivative of Matrix Determinant and Inverse
Description
MIT 18.S096 Matrix Calculus For Machine Learning And Beyond, IAP 2023
Instructors: Alan Edelman, Steven G. Johnson
Uploaded 01-12-23
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: The first ~6 minutes are on the topic Norms and Derivatives: Why a norm of the input and output are needed to define a derivative. Now we can find the “matrix gradient” of the determinant function (leading to the “adjugate” matrix), and the “Jacobian” of a matrix inverse.
License: Creative Commons BY-NC-SA
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