MIT 18.S096 Matrix Calculus For Machine Learning And Beyond, IAP 2023 - Lecture 5 Part 3: Differentiation on Computational Graphs

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: A very general way to think about the chain rule is to view computations as flowing through "graphs" consisting of nodes (intermediate values) connected by edges (functions acting on those values). When we propagate derivatives through the graph from inputs to outputs, we get the structure of forward-mode automatic differentiation; going from outputs to inputs yields reverse mode, which we will return to in lecture 8. 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.