Dope Tech Episode 2

<<@zachstar says : The first 500 people to use my link https://skl.sh/zachstar02251 will get a 1 month free trial of Skillshare!>> <<@Clemm_mm says : What about the whole ellipse rotation and shear thing in svd. That's what really confused me.>> <<@HelloIAmAnExist says : I... Got all that? Somehow? I legit have learned nothing about matrices before. I was going in expecting to maybe get the gist of like half of it.>> <<@stevejin9459 says : This compression is for 2d data, how about 1d data. How does the svd principle apply to 1d data(is 1d a special case of 2d)?>> <<@hardcoremc7053 says : I was taught this In School Good Job by them>> <<@4120_PRATIKRATHOD says : 6:03 Light bulb moment, you know svd but what to do with it, suddenly at this particular moment everything just makes sense>> <<@numbersix8919 says : Thank yew mister.>> <<@LilithFox-b4w says : It is so bizarre hearing the same voice as in the comedy skits, because I keep expecting punchlines then remembering... this is just an educational video. Lol>> <<@Deejay_quest says : I don't get this skit, I'm not gonna lie it wasn't very funny. And I got lost in the plot.>> <<@mcalebc says : how does this work if the matrix sigma has complex numbers in it or if sigma is in jordan canonical form>> <<@AntonRellik1123 says : can you do this in reverse? what about scaling a image? adding information to the matrix when it expands fill the space to the closest value of the original matrix>> <<@Wolkenphoenix says : Such a cool video, thank you <3>> <<@mightymcphee says : wtf when did you go from funny shorts a few years ago to explaining compression better than anyone I've seen. 2:53 this here is a good example of why this video and the script is so good. wow most impressed I've been with a presentation in years.>> <<@TheBestDog says : WTF? 🤬>> <<@MichaelKolczynski says : Only on the internet can you take 25 bits, turn them into at least 9 bits per component for a storage size of 180 bits and call it compression>> <<@dermasmid says : Thank you skillshare>> <<@MaeliaMBT says : Bruh, your timing is almost a bit spooky. I'm taking a course in machine learning this semester and a couple of weeks ago the lecture was about svd. But there it was in the context of principal compenent analysis, not data compression>> <<@GuyMichaely says : Would've appreciated more in depth explanation>> <<@SamratDuttabdn says : He is both WILLING and ABLE to teach us how to compress an image with (basic) linear algebra>> <<@limikeli9761 says : Hey Zack, what app or software did you use to create the pixel shading?>> <<@Al-Hebri says : Inspiring, Thank you>> <<@pawejerzyna5674 says : lovely video>> <<@Impatient_Ape says : OMG, thank you for talking about "rank" correctly. Physics and engineering often use the word "rank" when they actually mean "degree". Rank-1 matrices are representations of "pure" tensors. And all tensors of higher rank can be written as linear combinations of pure tensors.>> <<@Fereydoon.Shekofte says : Thank you very much Greatest explanation on YouTube ❤❤🎉🎉😊😊>> <<@eswyatt says : Seems so similar to PCA or principal component analysis.>> <<@LuisLascanoValarezo says : Thanks Zack for making this videos!! You know, a comedy video (which yours are great) I will watch once, but a great engineering video I'll watch as a prayer every time in need with connection with Mather Nature>> <<@50secs says : Nice video Zach, I wrote a blog on this topic as well few years ago. To demonstrate how the matrices takes the form of data on MNIST dataset to explain the generalisation process.>> <<@besthero12345 says : hey zach, r u still pursuing applied math as a degree?>> <<@user-pw5do6tu7i says : Getting all sorts of ideas around multi sampling windows here>> <<@user-pw5do6tu7i says : Bro is like finding the 'prime factors' but of a matrix. Freaking nerd>> <<@seedmole says : Nice, basically Fourier stuff but on images.>> <<@timmygilbert4102 says : That just generative ai 😂>> <<@featureboxx says : interesting, I was just going through a linear algebra course I recognize column spaces>> <<@littlestewart says : The guy who makes dumb skits uploaded a STEM video>> <<@nnm35 says : I wish you had been my math professor. Super clear explanations! Tnx!>> <<@awertyuiop8711 says : Now the real question is “How to compress an image with (basic) Geometric Algebra?”>> <<@delec9665 says : Clearest video on svd gg>> <<@jonathan3488 says : Thanks! I couldn't understand why we sort the eigenvalues by descending order in order to do low-rank approximations, but when I know that SVD leads to a linear combination of rank1 matrices, it became very clear. How cool!>> <<@Vytor_01 says : having this video recomended after making a 3d renderer wasnt an smart idea, youtube. Im way too tired of matrices>> <<@gooliberdoober says : H>> <<@derekpowersblight says : DLSS is wild>> <<@sachininthetube says : Thanks!>> <<@MasterHigure says : I did an SVD compression of a black-and-white image back in my intro linear algebra class. The fact that I still remember working on it almost 20 years after means I think it was cool.>> <<@Hashi201 says : I came here to know something BASIC But it's ADVANCED for me>> <<@shekarlakshmipathi says : Wish you and Grant (3Blue1Brown) were my teachers in high school. My heart starts beating fast when I start watching your videos. It is so exciting. I learn something new EVERYTIME.>> <<@vaidphysics says : Exactly this method is used in tensor networks for compressing information about a very complicated many body quantum state into a smaller set of variables. Great video 👏👏👏>> <<@ready1fire1aim1 says : It's super strange how reality is 3D but our logic, math and physics formalisms are 2D (bivalent, binary and third-person). We even explicitly reject 3D with the Law of Excluded Middle. It appears that Einstein should have squared Leibniz's first-person 0D-3D rather than Newton's third-person 3D-1D. Leibniz started from 0D and built up but Newton started from 3D and reduced down. If we start from non-zero dimensions and try to reduce down to 0D then we only get to 1D (also we get to all our current major/minor open problems, contradictions and paradoxes). The difference between 0D and non-zero dimensions is equivalent to the difference between P and NP. Leibniz's metaphysical monads and CS' mathematical monads have equivalencies, too, and I'd imagine that's how we get AGI. Einstein should have done 0D-3D² for 3 sets of 3 dimensions: space, time and energy (stars are observable 3D energy). Newton's 3D-1D can't reach 0D since that's not how reality works. 0D quarks form 3D protons and neutrons, not vice versa. Also we probably shouldn't think of our quarks as a 2D disc with a triangle of quarks but rather as a 3D sphere with a tetrahedron of quarks (we just can't see the 4th quark since it's on the other side of 0D).>> <<@SystemsMedicine says : Very clear, very lovely…>> <<@wintutorials2282 says : This is so awesome Thanks for the awesome video>> <<@sadeepweerasinghe says : you sound just like the guy from the funny videos>>