Support Vector Machines: All you need to know!

[@CJTongue]

That was awesome. Everyone always jumps to neural networks, but often svms do the same classification work with no huge iterative training required and are simply a better solution much of the time. Thanks for explaining the mathematical basis behind their power, and particularly the gaussian kernel function, so simple and yet such amazing results.

[@kottybeats]

Best video on KZbin about this topic! You explained everything that others pushed to the side with "this is beyond the scope of this video"! Thanks

[@convolutionalnn2582]

Its hurt when you don't even understand this kind of explanation 😭

[@almogdavid]

Don't feel ashamed it's not for someone who does not know SVM at all.

[@lam-thai-nguyen]

yeah I'm crying rn

[@HistoryUnlocked-fi3er]

It’s not the best explanation look at visually explaineds video

[@_________________404]

You need multivariable calculus to fully understand the math behind this. Linear algebra and univariate calculus is not enough. And even then this kind of thing is not something you can understand in 12 minutes. Even if you have all the prerequisites it's more like several hours if not days of work.

[@leslittlethings]

So concise, so intuitive, yet mathematically in-depth as well! Tysm!

[@karl777-7]

this is the only one i found trying to explain mathematically, thanks a lot!

[@pawmy4519]

I really love the Graphics and All the animation going around, it helped a LOT with my ADHD, and it gives me hope seeing people such as you go this far to make learning pleasing!

[@vats6]

Wow! Definitely one of the most clear intution of SVMs availble on the internet, Thank you sir.

[@Hammadmk971]

I love you, man! I went through several explanations, books, guides but couldn't understand the underlying concept. You made it crystal clear! Thank you very much!

[@arth01r]

This is one of the clearest explanations in 15 minutes!

[@shenhengyu]

That is very good explanation. The mathematical deduction part is fantastic.

[@Vijay-fi8bm]

I understood the whole SVM concept with just one video, that too with visualisations.

[@schnittstelle8492]

You deserve like 1 Million subscribers for your contents, I am telling you that!

[@IntuitiveMachineLearning]

Thanks!!

[@mulls9281]

This is an amazing video. Usually these KZbin videos leave out the math and focus on the animation. But clearly the mathematician/animator of this video is very talented.

[@sianashsad7805]

Oh my god, this video is so well made it's actually mind blowing.....

[@niranjansitapure7752]

This was the best and most quick SVM video.

[@DJSturm1]

First video on youtube about svm that gathers together everything you need to know about this algorithm

[@milanspanko241]

This video fully covers the range of the SVM lecture at my university, something that others video don't even come close to. Perfect !

[@chandanadeeksha884]

All of that in 15 minutes. My mind is blown 😲Thanks for the video

[@kishmatbhattarai]

This is most amazing and actual explanation of SVM in entire internet. Thank you ❤️

[@felixkania498]

Getting even better with every upload!

[@IntuitiveMachineLearning]

Thanks!

[@felixkania498]

Intuitive Machine Learning do you have some further recommendations for learning about ML? Thanks!

[@IntuitiveMachineLearning]

Stay tuned on our channel😊

[@lh24-g7l]

Thanks for the brilliant video!

[@tunamusic2314]

giau

[@yezhang9746]

The best machine learning channel!

[@IntuitiveMachineLearning]

Thanks! 😊

[@geeteshpaidi200]

i did not understand this video when I saw it first but then I took a pen and a paper and did the proof along with him. Now i understood the maths behind it. Thank you.

[@sheetsoncrack6666]

The best SVM explanation video I have ever watched

[@mohammadamjadi641]

HATS OFF TO YOU . THE BEST VIDEO I HAVE EVER SEEN ABOUT SVM . THANNK YOU

[@itsfabiolous]

The math is soooo good explained. I think the pacing is hard without prior knowledge but you explained that so damn elegantly! Thank you!

[@Jack-cm5ch]

I am glad I found this channel

[@RashidGoulebe]

You safed my grade! Thank you!

[@kanikaverma3673]

underrated video, too good thank you so much

[@peterdr2727]

This is supper cool! Best explanation!

[@IntuitiveMachineLearning]

Thanks very much!

[@rahulduvedi3341]

That was epic!! Please making more of these

[@IntuitiveMachineLearning]

Thanks very much!

[@DigitalMadness]

I love this channel!

[@IntuitiveMachineLearning]

Thanks!

[@supersnowva6717]

After watching the Support Vector Machine lecture from MIT Professor Patrick Winston, it makes so much sense! One of the best explained videos on SVM! Although I think there is a typo on the Lagrangian equation, the sign of the first term SUM(alpha _i) should be "+" instead of "-". Otherwise perfect!

[@leslittlethings]

At 9:07 I think there's a problem with signs? Or prolly missing brackets.

[@priyankagehlot9945]

You are doing great man👍 Keep going ❤

[@jlee-mp4]

This is one of the best videos ever

[@visheshsinha_]

This is Great, so simple explanation. Thank you

[@IntuitiveMachineLearning]

thanks！

[@javiergil8561]

Extremely high quality content! I've already shared it on linkedIn... By the way, what software do you use for graph animations? Keep it up!

[@IntuitiveMachineLearning]

We use adobe premiere for video editing

[@javiergil8561]

@@IntuitiveMachineLearning Adobe Premiere for graph animations too?

[@eachannel8341]

Great job. It must have taken a lot of time. Thanks a lot!

[@waiyanpyaesone9749]

How come negative Sum alpha sub i * negative 1 = negative Sum alpha sub i at 9:12 ?

[@chengwei32]

I think there are multiple places the signs are not correct around the time mark. One more thing, around these deviations, I think it is better not to use so much animation, or do the animation slowly. It is too difficult to see it clearly. But the content of the video is very high quality. Thanks a lot for sharing it.

[@ramprasathasokan4537]

Way to go.. It's simply awesome..

[@IntuitiveMachineLearning]

thanks!

[@sjnoh63]

Fantastic explaination!! Such great representation of concepts. thank you so much!

[@bhaveshsharma9087]

Bro u r doing great keep it up

[@IntuitiveMachineLearning]

thanks

[@hongkyulee9724]

Thank you for the nice video. I wanna contribute to the world like you. Hope your happiness!

[@eileend4072]

Pretty nice content. I'm watching Andrew's Ng ML class and this video walks through the steps that were skipped by Ng. Off topic, I'm guessing the narrator speaks Mandarin

[@deutschvalley3574]

Great explanation sir i love they way of teaching kindly make videos on all Ml and Deep learning Algorithms 🤩

[@ashishmishra5233]

Well done Sir, thanks for the effort 🤘

[@cleytondesouzaoliveira5280]

Very helpful video. Thank you!

[@govindbarwa1806]

Very high quality video it is

[@starkt1554]

I hate that this channel is underrated

[@antoniomajdandzic8462]

Clear explanations. I encourage you to produce more content. :)

[@rajesht9702]

Thank you for making this video

[@theAi_journey]

I love it that you did not leave math part

[@zukofire6424]

amazing, have to say : thank you very much!

[@linzhu5178]

amazing, thanks laotie

[@damoonrobatian9371]

Great job!

[@yourdudecodes]

This video helps me sleep at night.

[@hindeshakash4709]

Simply Brilliant!!!

[@tomsze4314]

I felt lucky after watching this video

[@yuqingxue8518]

I LOVE THE VIDEO. It's better than my professor LOL

[@uahnbu]

The art style is similar to a comic I've read. Does anyone know what it is?

[@dylantintenfich845]

Love this video! Only one question. Is the partial derivative ∂L/∂w the same as the gradient ∇w L?

[@machkouroke7731]

How to extends support vector machine to multi label classification?

[@Pankaj_Khanal_Joshi]

Pls teach maths behind ML. Thank you

[@MrFischvogel]

Awesome !!

[@adrenochromeaddict4232]

I'm so glad you made this video the statquest guy can't teach shit

[@yaserdavidcruzdelgado182]

Thanks a lot!

[@machkouroke7731]

What is the unknow vector u?

[@hrsight]

Good content

[@dennyloevlie768]

Great video but at the beginning you say that SVM is a nonlinear model when it is a linear one.

[@user-sultan-el-hadra]

you are the best

[@sumanthkumar4035]

it's a great video, but i didn't understand how (2/||w||) is equal to 0.5*||w||^2 . TIA

[@Adi-eh1gp]

Those are not equal. We want to maximize 2/||w|| (this is the distance between H1 and H2). Maximizing this is equivalent to minimizing ||w||, which is equivalent to minimizing 0.5*||w||^2. :)

[@iamavolk]

Great potential, but throwing some important mathematical concepts (that actually build the intuition) around like it's something trivial is careless at best. Either the channel name then should be non-intuitive ML or gotta expand on things like "for mathematical convenience" -- that is, convexity + computational / numerical optimization -- things that are way more important to emphasize for intuition-building purposes than technical use of Lagrange multipliers

[@AndreiLenrelGeraldino]

can i used your PPT sir ? i have my reporting tomorrow about SVM please can someone help me send ma a PPT? of this?

[@ahmetsezginn]

thanks mate

[@nelsondelarosa5490]

Exacellent

[@starkt1554]

The best

[@wishIKnewHowToLove]

Thx

[@Elitea20]

If I don’t understand this explanation….maybe I’m the problem

[@Elitea20]

I accepted my fate about failing my machine learning test

[@radioactivespaghetti3416]

This was a really good video but this dudes English was so hard to understand at some points I had to keep subtitles on😭

[@eileneanna440]

too much ads

[@ismailkaracakaya260]

Nobody explains what is w? where is it coming from, or what is b which is the intercept, how to calculate it , terrible video

[@Ramon314]

A.K.A.*

[@martinkunev9911]

Good explanation, but I really have trouble understanding your accent.

[@PrasanNavi]

This is good but i think you are going too much into the math which is unnecessarily making this video very long.

[@HolyRoody]

Man you gotta skip explaining things mathematically and do it in other ways, a lot of us students don't even understand math.

[@ritshpatidar]

I lost it in the first equation itself🥲. Even Transformers are easier to understand.