درباره این اپیزود
MIT 18.S096 Matrix Calculus For Machine Learning And Beyond, IAP 2023
Instructors: Alan Edelman, Steven G. Johnson
Uploaded 23-10-23
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: Even if a function f is stochastic (has random numbers in it), we can still define a derivative “on average” (whose expectation value is the derivative of the expectation value of f). For example, for stochastic gradient descent in machine learning.
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.
Learn more about your ad choices. Visit megaphone.fm/adchoices
Learn more about your ad choices. Visit megaphone.fm/adchoices
