When I was 10, I got up early on a Saturday for the Smurfs. Now, I get up early for Sparsity. No commercial interruptions. All that's missing is some Lucky Charms.

@TheMazyProduction

3 жыл бұрын

For me it was spongebob how it’s generative models.

Life would be so much better when they teach about applications before dry theory as motivation. I am very thankful for these amazing videos.

@Eigensteve

3 жыл бұрын

Couldn't agree more!

Thank you Steve. Your KZread videos are a great balance of accessibility of quick bite-sized concepts without the lowering of discussion to menial examples.

Thank you so much, Steve! You're connecting the practical implementations with the underlying math incredibly seamlessly, almost a work of art!

Thank you Professor Steve. Just what I was looking for! Your tutorials have helped me in my masters in Control engineering, and this new series is a helping hand at the start of my PhD.

Many thanks indeed for providing such great lectures and sharing it with us. :)

Dear Steve... your lectures are a blessing. Thank you so much 🙏

Omg, I'm completely in love for signals because of you!! Hope doing something cool soon with all this!

Thank you very much for these amazing and well detailed videos, I'm actually working on my Master's degree thesis about cognitive radio and these videos helped a lot in the compressed sensing chapter!

Thank you so much for the lectures!

Hi Professor Steve, Thanks for the nice idea shear with us.

You just made it super interesting. Hats off.

Thank you for the video, great as usual

Obrigado, Steve! Things make more sense now.

love those lectures

Dimensionality reduction is simply awesome!

Useful and concise

Holy shit man, I just discovered your channel and it feels like I have found a gold mine! There are soo many useful videos that I don't even know where to begin. Is there any way to support your work so you can continue creating this wonderful content? Patreon, Paypal, Donations?

Amazing explanation!! :)

What a time to be alive!

Thanks professor.

Thank You !

Man u look like HG Wells from flash series ....luv ur videos ❤

I just wish this was available 30 years back when I worked on video compression. Great material and well presented indeed. But as far as I recall JPEG relied on DCT, not DFT.

@Andres186000

2 жыл бұрын

I think JPEG may have originally relied on DFT and then was updated to DCT to deal with gibbs phenomena when compressing images with sharp edges

7:30 When the metalhead in you kicks in \\m// :) One question, what is a quantitative measure of sparsity? Like how much percentage of elements should be zero to count a matrix as sparse?

Thank you for the video! very helpful! is it possible that some basis gives us a more sparse result than others? Like here the DFT may give us 10 non-zero entries of a 1mil entry image, but what happens if we use a different transform that gives us 5 or 20 non-zero entries? Does that make a difference and is it in our interest to look for transforms that keep increasing the sparsity?

Thanks for these amazing videos! I really appreciate if you could talk a little bit about L0 norm also. Thank you!

In Tailored Basis what is epsilon r and Vr?

Is the the sum of the values in the sparse S 'equals to 100%' like they are the whole values that play a role in the equation ?

Thank you very much

@Eigensteve

3 жыл бұрын

You are welcome

super super interesting

I suspect sparsity and structure might be the same thing. I think that translates to a high degree of linear independence being more informative. Then information is something which is less dependent on circumstance.

Just out of curiosity, are you writing backwards, or are you flipping the image with software? Very interesting and effective video making setup.

@SridharNag

3 жыл бұрын

same here, even i would like to know the same

@yosily

2 жыл бұрын

I had the same question

steve brunton in 1440p oh my god

for what its worth jpeg uses discrete cosine transform and not discrete fourier transform - awesome vids tho

But how is he writing everything backwards so easily...?