Lecture 2 Part 1: Derivatives in Higher Dimensions: Jacobians and Matrix Functions

MIT 18.S096 Matrix Calculus For Machine Learning And Beyond, IAP 2023
Instructors: Alan Edelman, Steven G. Johnson
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Description: Derivatives as linear operators give a fresh perspective on multivariable calculus, gradients, and Jacobian matrices; but we can now generalize to matrix functions.
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[@rw7154]

This explanation of backpropagation is better than any other CS professor’s videos on the web. Steven really went straight to the heart of the issue and showed its beauty and simplicity at the same time.

[@arjunsigdel8070]

Massive respect when instructors interact with each others.

[@CashMan-n5s]

Such a complex field discussed in such a clear fashion. Thank you professors.

[@nithingeorge9025]

This has such a good introduction to the idea of backpropagation! Looking forward to learning more now. Kudos for this 2-teacher model as well.. Grateful to be able to access this lecture for free!

[@ItahangLimbu]

Is that Philip in the background? Buddy looks chill

[@zhaoxingdeng5264]

Great course!

[@kiku000]

its kinda odd that the students are barely participating in these questions. i wonder why

[@lchunleo]

Excellent lecture. Learnt alot from mathematical perspective

[@jiehe6943]

It took me 30min to prove ax as the only linear operator on R. Feel I’m so dumb

[@harshavardhanlakhinana5280]

lecture 3 for today and am going to complete it

[@nicolasg.b.1728]

We should normalize seeing corgis paying careful attention to math classes.

[@Aikman94]

Phillip!