SVM (The Math) : Data Science Concepts

Let's get mathematical.
SVM Intuition Video: • Support Vector Machine...

[@stanlukash33]

This guy is underrated for real. KZbin - throw him into recommendations.

[@jmspiers]

I know... I recommend him all the time on Reddit.

[@backstroke0810]

True! He deserves way more subscription. He should prepare a booklet like statquest did but of his own. Would definitely buy it!

[@aravind_selvam]

True!!

[@supersql8406]

This guy is super smart and he takes sophisticated concepts and explains it in a way where it's digestible without mocking the theory! What a great teacher!

[@ragyakaul6027]

I can't explain how grateful I am for your channel! I am doing an introductory machine learning course at Uni and it's extremely challenging as it's full of complex concepts and the basics aren't explored throughly. Many videos I came across on youtube were too overly simplified and only helped me very briefly to make sense of my course. However, your videos offer the perfect balance, you explore the complex maths and don't oversimplify it, but do so in a way that's easy to understand. I read through this concept several times before watching your video, but only now do I feel as if I TRULY understand it. I HIGHLY appreciate the work you do and look forward to supporting your channel.

[@maged4087]

same

[@velevki]

You answered all the questions I had in mind without me even asking them to you. This was an amazing walkthrough. Thank you!

[@pavelrozsypal8956]

Another great video on SVM. As a mathematician I do appreciate your succinct yet accurate exposition not playing around with irrelevant details.

[@FPrimeHD1618]

Just to add onto all the love, I'm a data scientist in marketing and you are my number one channel for reviewing concepts. You are a very talented individual!

[@srivatsa1193]

This is the best and the most intuitive explanation for SVM. It is really hard for me to actually read research papers and understand what story each line of the equation is telling. But you made it soo intuitive. Thanks a ton! Please Please make more videos like this

[@shusrutorishik8159]

This has been simultaneously the simplest, most detailed and yet most concise explanation of this topic I've come across so far. Much appreciated! I hope you keep making awesome content!

[@ritvikmath]

Glad it was helpful!

[@vedantpuranik8619]

This is the best and most comprehensible math video on hard margin SVM I have seen till date!

[@KARINEMOOSE]

I'm a PhD student studying data mining and I just wanted commend you for this SUPERB explanation. I can't thank you enough for the explaining this so clearly. Keep up the excellent work!!

[@nikkatalnikov]

Great video as usual! A possible side note - I find 3d picture even more intuitive. Adding z-direction which is basically can be shrunk to [-1;1] is our class prediction dimension and x1 x2 are feature dimensions. Hence, the margin hyperplane "sits" exactly on (x1; x1; 0) This is also helpful for further explanation of what SVM kernels are and why kernel alters the norms (e.g. distances) between data points, but not the data points themselves.

[@stephonhenry-rerrie3997]

I think this might be top 5 explanations of SVM mathematics all-time. Very well done

[@yangwang9688]

Very easy to follow the concept! Thanks for this wonderful video! Looking forward to seeing next video!

[@nickmillican22]

Question on the notation. The image shows that the vector between the central line and decision line is w. So, I think, that w is the length of the decision boundary. But then we go on to show that the length of the decision boundary is k=1/||w||. So I'm not clear on what w (or k, for that matter) are actually representing.

[@WassupCarlton]

I too expected k to equal the length of that vector w :-/

[@usmanabbas7]

You and statquest are the perfect combination :) Thanks for all of your hardwork.

[@luchomame1]

Dude thank you! now these equations don't feel like they were pulled out of thin air. and the best part is I can work them out too! I haven't done linear algebra in almost a decade so I got stuck on the ||w||/(w*w) part for a good bit but this pushed me to refresh some concepts and figure it out! Thank you

[@lisaxu1848]

studying my masters in data science and this is a brilliant easy to understand explanation tying graphical and mathematical concepts - thank you!

[@polarbear986]

I finally get svm after watching a lot of tutorial on KZbin. Clever explanation. Thank you

[@clifftondouangdara6249]

Thank you so much for this video! I am learning about SVM now and your tutorial perfectly breaks it down for me!

[@Shaan11s]

your videos are what allowed me to take a spring break vacation bro, saved me so much time thank you

[@ritvikmath]

Great to hear!

[@mindyquan3141]

So simple, so clear!!! Wish all the teachers are like this!

[@honeyBadger582]

That's what i've been waiting for! Thanks a lot. Great video!

[@ritvikmath]

Glad it was helpful!

[@nishanttailor4786]

Just Amazing Clarity of Topics!!

[@gdivadnosdivad6185]

I love your channel. You explain difficult concepts that could be explained to my dear grandmother who never went to college. Excellent job sir! You should become a professor one day. You would be good.

[@lakhanpal1987]

Great video on SVM. Simple to understand.

[@aashishkolhar8155]

Great, thanks for this lucid explanation about the math behind SVM

[@jaibhambra]

Absolutely amazing channel! You're a great teacher

[@zz-9463]

very informative and helpful video to help understand the SVM! Thanks for such a great video! You deserve more subscribers

[@houyao2147]

It's so easy to understand thi s math stuff! Best explanation ever in such a short video.

[@TheWhyNotSeries]

At 5:10, I don't get how you obtain K from the last simplification. Can you/someone please explain? Btw beautiful video!

[@ritvikmath]

thanks! I did indeed kind of skip a step. The missing step is that the dot product of a vector with itself is the square of the magnitude of the vector. ie. w · w = ||w||^2

[@TheWhyNotSeries]

@@ritvikmath right, thank you!!

[@badermuteb4552]

Thank you so much. This is what i have been looking for so long time. would you please do the behind other ML and DL algorithms.

[@ht2239]

You explained this topic really well and helped me a lot! Great work!

[@emid6811]

Such a clear explanation! Thank you!!!

[@chimetone]

Best high-level explanation of SVMs out there, huge thanks

[@ritvikmath]

Glad it was helpful!

[@maheshsonawane8737]

🌟Magnificient🌟I actually understood this loss function in by watching once. Very nice explanation of math. I saw lot of other lectures but you cant understand math without graphical visualization.

[@techienomadiso8970]

This is a serious good stuff video. I have not seen a better svm explanation

[@ifyifemanima3972]

Thank you for this video. Thanks for simplifying SVM.

[@more-uv4nl]

this guy explained what my professors couldn't explain in 2 hours 😂😂😂

[@user-ik5vu8rf9d]

Thanks man great explaination , was trying to understand the math for 2 days , finally got it

[@ritvikmath]

Glad it helped!

[@dcodsp_]

Thanks for such brilliant explanation really appreciate your work!!

[@WassupCarlton]

This is giving "Jacked Kal Penn clearly explains spicy math" and | am HERE for it

[@sejmou]

In case you're also having trouble figuring out how we arrive at k=1/||w|| from k * (w*w/||w||) = 1: remember that the dot product of any vector with itself is equal to its squared magnitude. Then, w*w can also be expressed as ||w||^2. ||w||^2/||w|| simplifies to just ||w||. Finally bring ||w|| to the other side by dividing the whole equation by ||w||, and you're done :) if you also have trouble understanding why exactly the dot product of any vector with itself is equal to its squared magnitude it also helps to know that the magnitude of a vector is the square root of the sum of squares of its components and that sqrt(x) * sqrt(x) = x I hope that somehow makes sense if you're struggling, surely took me a while to get that lol

[@FootballIsLife00]

I almost forget this rule, thank you brother for saving my day

[@SreehariNarasipur]

Excellent explanation Ritvik

[@himanshu1056]

Best video on large margin classifiers 👍

[@ziaurrahmanutube]

Amazing explanation from the theoretical to the mathematical. Please tell me how you do it? So i can self-learn myself how you are able to understand and then explain these concepts or other concepts. what resources do you use ?

[@BlueDopamine]

I am very happy that I found Your YT Channel Awsome Videos I was unable to Understand SVM UntilNow !!!!

[@learn5081]

very helpful! I always wanted to learn math behind the model! thanks!

[@maurosobreira8695]

Amazing teaching skills - Thanks, a lot!

[@TheOilDoctor]

great, concise explanation !

[@mensahjacob3453]

Thank you Sir . You really simplified the concept. I have subscribed already waiting patiently for more videos 😊

[@godse54]

Pls also make one for svm regression.. you are amazing

[@NiladriBhattacharjya]

Amazing explanation!

[@AnDr3s0]

Nice explanation and really easy to follow!

[@Jayanth_mohan]

This really helped me learn the math of svm thanks !!

[@asharnk]

What an amazing video bro. Keep going.

[@jingzhouzhao8609]

thank you for your genius explanation. At 5:11, before getting the value k, the equation k * ( w * w) / (magnitude of w) = 1 contains w * w, why the output k doesn't have w in the end.

[@zarbose5247]

Incredible video

[@trishulcurtis1810]

Great explanation!

[@yashshah4172]

Hey Ritvik, Nice video, can you please cover the kernalization part too.

[@harshalingutla7318]

brilliant explanation!

[@akashnayak6144]

Loved it!

[@Snaqex]

Youre so unbelieveble good in explaining :)

[@superbatman1462]

Easily Explained 👍, Can you also explain how does SVM works with respect to regression problems?

[@pedrocolangelo5844]

Once again, ritvikmath being a lifesaver for me. If I understand the underlying math behind this concepts, it is because of him

[@borisshpilyuck3560]

Great video ! Why we can assume that right hand side of wx - b in those three lines is 1, 0, -1 ?

[@SESHUNITR]

very informative and intuitive

[@amanbagrecha]

what about the points within the margin? are they support vectors as well?

[@fengjeremy7878]

Hi ritvik! I wonder what is the geometric intuition of the vector w? We want to minimize ||w||, but what does w look like on the graph?

[@walfar5726]

Very well explained, thank you !

[@sorrefly]

I'm not sure but I think you forgot to say that in order to have margin = +-1 you should scale multiplying constants to w and b. Otherwise I don't explain how we could have distance of 1 from the middle The rest of the video is awesome, thank you very much :)

[@bhuvaneshkumarsrivastava906]

Eagerly waiting for your video on SVM Soft margin :D

[@germinchan]

This is very clearly defined. Thank you. But could someone explain to me what w is? How can I visualize it and calculate it.

[@rndtnt]

Hi, how exactly did you choose 1 and -1, the values for wx -b where x is a support vector? wx-b = 0 for x on the separating line makes sense however. Could it have other values?

[@sukritgarg3175]

Holy shit what a banger of a video this is

[@Reojoker]

Are SVMs only useful for binary classification, or can they be extended to multi-class predictions?

[@wildbear7877]

You explained this topic perfectly! Amazing!

[@ritvikmath]

Glad you think so!

[@kanishksoman7830]

Hi Ritvik, you are a great teacher of stats, calculus and ML/DL! I have one question regarding the equations. Why is the decision boundary equation W.X - b = 0? Shouldn't it be W.X + b = 0. I know the derivations and procedure to find the maximal margin is not affected but I don't understand -b. Please let me know if the sign is inconsequential. If it is, why is it? Thanks!

[@fengjeremy7878]

Thank you! I am wodering why do we use "+1 and -1" instead of "+1 and 0" to classify these two areas?

[@user-wr4yl7tx3w]

Wow, that was so well explained.

[@suckockshititties2599]

You are an amazing elucidator👍

[@stephanecurrie1304]

That was crystal clear !

[@robfurlong8868]

@ritvikmath - Thanks for this great explanation. I have noticed other material online advises the equation for the hyperplan is w.x+b=0 rather than w.x-b=0. Can you confirm which is accurate

[@joyc5784]

On the other references they use the plus (+) sign on w x - b = 0. Why on your example this was changed to minus sign? w x - b = 0. or wx - b > 1. Hope you could answer. Thanks

[@zeinramadan]

great video as always. thank you

[@ritvikmath]

Glad you enjoyed it!

[@TheCsePower]

You should mention that your W is an arbitrary direction vector of the hyperplane. (it is not the same size as the margin)

[@kmishy]

How you chose equation of two blue parallel lines? I mean how did you get 1 in the upper line and -1 for bottom line?

[@user-bp5go3ds5t]

phenomenal

[@ramankutty1245]

Great explanation

[@blackforest449]

So good .. ThnQ

[@salzshady8794]

Could you do the math behind each Machine learning algorithm, also would you be doing Neural Networks in the future?

[@marthalanaveen]

along with the assumptions of supervised and un-supervised ML algorithms that deals specifically with structured data.

[@ritvikmath]

Yup neural nets are coming up

[@jjabrahamzjjabrhamaz1568]

@@ritvikmath CNN's and Super Resolution PLEASE PLEASE PLEASE

[@mykhailoseniutovych6099]

Great video, with easy to follow explanation. However, you formulated the optimization problem that needs to be solved by the end of thevideo. The most ineteresting question now is how to actually solve this optimization problem. Can you give some directions on how this problem is actually solved?

[@madshyom6257]

Bro, you're a superhero

[@Pazurrr1501]

BRILLIANT!

[@almonddonut1818]

Thank you so much!

[@xviktorxx]

Great video, great underappreciated channel! Thank you and keep up the good work!

[@TheCsePower]

Great Viideo!. I found your notation for x to be quite confusing. I think the small x should be x11 x12 x13 to x1p. Say GPA is xi1 and MCAT is xi2. Then the student data for these two features will be: student 1(x11,x12) student 2 (x21, x22) student 3(x31,x32)

[@maged4087]

does b affect finding the slope?

[@user-hu5qf6lg8u]

you are my savior

[@acidaly]

Equation for points on margins are: w.x - b = 1 w.x - b = -1 That means we have fixed our margin to "2" (from -1 to +1). But our problem is to maximize the margin, so shouldn't we keep it a variable? like: w.x - b = +r w.x - b = -r where maximizing r is our goal?

[@davud7525]

Have you figured it out?

[@Max-my6rk]

Smart! This is the easiest way to come up with the margin when given theta (or weight)... gosh..