Linear Regression 2 [Python]

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This video describes how the singular value decomposition (SVD) can be used for linear regression in Python (part 2).
Book Website: databookuw.com
Book PDF: databookuw.com/...
These lectures follow Chapter 1 from: "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz
Amazon: www.amazon.com...
Brunton Website: eigensteve.com
This video was produced at the University of Washington

Опубликовано:

16 сен 2024

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Комментарии : 7

@marnesh2008 Год назад

This is crazy outstanding way to teach i just wish all the online classes from the universities are like this

@tjarko72 3 года назад

Very nice series, I got suck into it. But in the python examples that `np.linalg.inv(np.diag(S))` line first makes a square matrix and then inverses it. I don't know how smart linalg is in this case, but first inversing and then making a matrix conceptually looks faster. e.g. `np.diag(1/S)'

@pk00982 2 года назад

Hi Steve, Great videos! Thank you for creating them. In your SVD videos you speak about X where the num samples (m) is along the columns while computing U, S and VT. But for solving Ax=b, the num samples (m) for A is along the rows. Would that effect how we compute SVD of A?

@_J_A_G_ Год назад

I think this discussion may clarify some. ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-_HTjxl_sdUA.html&lc=UgxQn0b8nzdD-vJrsyt4AaABAg

@Старкрафт2комедия 2 года назад

What i want to see is the mass on a spring oscillating with 3 cameras recording data. I can never figure out how to actually solve for the final spring equation using svd. I have seen it as an example but nobody ever gets past showing the sigma matrix. I wanna see the final step of going from that u sigma v to f=ma or whatever.