SVD: Image Compression [Python]

This video describes how to use the singular value decomposition (SVD) for image compression in Python.

These lectures follow Chapter 1 from: "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz

This video was produced at the University of Washington

[UploaderAMVz]

Your way to present your knowledge is outstanding. The combination of a whiteboard, yourself and the code is great!

[tyfyrb]

I rarely comment on youtube. But I have to say, you are by far the best teacher around here. This is the first channel I allowed the notifications. I want to watch every single of your amazing videos. I hope you never stop uploading.

[henrmota]

After learning linear algebra from scratch it's time to see this videos and apply the knowledge as a programmer. Thank you.

[maxmustermann-hxfx]

Our math professor just said as an example that when compressing an image "you can throw away the remaining numbers and only keep the large sigmas of a image" and I asked myself why. And now I know why thank you so much.

[zacharythatcher]

For anyone confused about why np.diag is necessary, it is actually turning a vector into a matrix here rather than the other way around. S actually comes out of np.linalg.svd as a 1-D array of length n, and therefore should become the diagonal of an empty matrix of size nxn in order to work nicely with these matrix multiplication @ operators. This is what np.diag is doing. Oddly it will go either way depending on if you give it a matrix or a vector.

[draganostojic]

Thank you so much for invaluable lectures and book!

[yuxiang]

This is truely impressive, better than any university lectures on these topics. One question: How do you deal with RGB images?

[nicolemontenegro]

Awesome!!! Thank you very much, merci bcp, gracias!!!! I loved your way of teaching. It is very understandable. This content is very useful for my Ph.D. Thank you

[s.tahsin.r]

Thank you so much for the resources, it's so good to have the code and use it ourselves.

[altruist_AI]

Thank you Steve I have always wanted to know more about SVD and how it helps in dimentionality reduction... Also I want to know used cases where it can play a major role.. thank you in advance

[hubstrangers]

Thank you, much better with the supporting code and how the maths is applied, certainly better than loading slides after slides...

[sayanbhattacharya]

Amazing work...Thank you very much for the help...Could you please expand on a different lecture sometime on Fista algorithm?

[zsun]

an image case of the approximation that SVD gets to in regards to the original image is easy to understand. It would be helpful to see a tall-skinny case like recording phases of an experiment.

[camilojarac]

Loved this video! Very good expanations. Thanks!

[bhuvansaikrishnapusala]

G.O.A.T Such a greattt explanation🎉🎉🙏🙏🙏

[eleugewp]

Amazing videos, very eloquent!!!! Could you tell me how you made them?

[马晓晓-iy]

I have to say these courses, slides, and presentations are so well prepared, thanks professor, and hope to learn more from you.

[akramz]

I'm This is exactly what i need thanks a lot

[kyoungsub]

thank you for sharing how it actually is implemented!

[David-tdik]

Hi Steve, enjoyed your session. here I have a questions about the svd. In many scenarios, I see people do svd on the covariance matrix or just simply svd (A^T * A) instead of svd(A). Can you explain why or why not to do so?