04:22 The main goal of SVM is to find the equation of a hyperplane with the maximum margin between positive and negative hyperplanes. 08:44 In machine learning, a decision rule can be derived using the equation w * u + b > 0. 13:06 The equation of the hyperplanes is assumed to be W transpose x + b = 1 for the positive hyperplane and W transpose x + b = -1 for the negative hyperplane. 17:28 The distance calculation involves simplifying equations to x squared and creating positive and negative lines for convenience. 21:50 Multiplying by a factor greater than 1 shrinks the margin, while multiplying by a factor less than 1 expands the margin. 26:12 Maximize distance between positive and negative hyperplanes with constraints 30:34 The distance between two support vectors in SVM can be calculated using the dot product of the vectors and the unit vector 34:54 The expression for distance calculation and the optimization function in SVM Crafted by Merlin AI.
All other RUclips videos just explain the basics, but no one explains the maths behind the SVM. But you explained the whole mathematics of SVM. I was stuck on understanding the logic behind the SVM, and even I read many books on ML, but I had not get brief explanation. But your video helps me a lot.
i am a PhD scholar at NIT in Mathematics this video is for actual working of SVM rather than those 10 min video whose aim is just views but the viewer doesn't know.
It's commendable how painstakingly you explain every single detail! Wish we had more professors like you. Thanks a ton for these videos, you're a life saviour..
Don't have words to thank you sir for this more than amazing explanation ! Tried multiple sources but it became more of rote learning but your video actually made me understand how SVM works ! Thanks a Ton Sir ! :)
The main goal of SVM is to find the equation of a hyperplane with the maximum margin between positive and negative hyperplanes. 04:22 In machine learning, a decision rule can be derived using the equation w * u + b > 0. 08:44 The equation of the hyperplanes is assumed to be W transpose x + b = 1 for the positive hyperplane and W transpose x + b = -1 for the negative hyperplane. 13:06 The distance calculation involves simplifying equations to x squared and creating positive and negative lines for convenience. 17:28 Multiplying by a factor greater than 1 shrinks the margin, while multiplying by a factor less than 1 expands the margin. 21:50 Maximize distance between positive and negative hyperplanes with constraints 26:12 The distance between two support vectors in SVM can be calculated using the dot product of the vectors and the unit vector 30:34 The expression for distance calculation and the optimization function in SVM Click to expand 34:54
Ammmazing guy i just wnated to know about maths of svm to write in my sem exam( b.e) and now i know much more than just that please keep doing tye great work
Thanks for giving such a good explaination on this topic on I was struggling to get clearity.It really helped me to get good picture of mathematical concept behind svm.
Bro your video is extremely good kudos to you. Just improve the sound quality as every thing is hazy while listening that is manageable. But overall awesome❤❤❤
pls anyone answer these question 1) the planes pie(+) and pie(-) are decided by the support vectors then how come their equation can be guaranteed to be wx+b=1 and wx+b=-1 2)if the equation are wx+b=1 and wx+b=-1 , the margin(d) we aiming to maximize is basically the distance bw these two planes only, and if we see the distance bw these two planes is 2 which is a constant then how come we can maximize a constant
you very casually said that projection is dot product, can you please provide any source where its written that dot product is actually projection of one vector over another
I have a doubt, pi+ and pi- are basically hyperplanes passing through the closet points from the hyperplane wx + b = 0(let's say hyperplane p), what if the distance between pi+ and p is not the same as pi- and p, there can be a case in which the distance between the nearest point of one class and the hyperplane p and the distance between the nearest point of the other class and hyperplane p may not be equal.....what to do in this kind of a case? And how do we optimize the distance function?
Hi sir, As we were trying to achieve hypothesis line in Logistic regression such that it achieves equilibrium among dataPoints. And in SVM also we are doing same thing . Then what is the difference/ how it is solving logistic regression's problem/ or what is the problem in Logistic regression when we have achieved Equilibrium hypothesis line ? @CampusX
The optimization problem to obtain the plane is different in each . In svm the loss function has two terms: margin and misclassification components. In logitreg, it's the binary cross entropy. So the method in which we obtain the hyper plane is different
perhaps there was a slight mistake it should be w.u - c = 0.. such that when any point is greater than 0 than it must lie in positive region else negative region... b and c are just contant for positive n negative hyperplane respectively.
Great video! One question, at 5:00 you made the decision rule vector(w).vector(u) + b >= 0. How did you put 'b' here which is a characteristic of the hyperplane? Thanks for clarifying in advance.
Bismillah Obviously, you need to know the intercept of the line. Only knowing the vector w will not help because without b, you don't know where the line is clearly situated. With vector w, you know the direction of the line but not it's exact postition. The line could have a gradient of 3, for e.g., but if you don't know where the line cuts the y axis, how can you make your decision?
ruclips.net/video/yCAlHPDgWtM/видео.html sir at this point of time you are multiplying equation by some constant but why right side is not changing ????
This video made me emotional ....I couldn't understand my professor's slides at all but now its damn easy. This channel is underrated for sure.
04:22 The main goal of SVM is to find the equation of a hyperplane with the maximum margin between positive and negative hyperplanes.
08:44 In machine learning, a decision rule can be derived using the equation w * u + b > 0.
13:06 The equation of the hyperplanes is assumed to be W transpose x + b = 1 for the positive hyperplane and W transpose x + b = -1 for the negative hyperplane.
17:28 The distance calculation involves simplifying equations to x squared and creating positive and negative lines for convenience.
21:50 Multiplying by a factor greater than 1 shrinks the margin, while multiplying by a factor less than 1 expands the margin.
26:12 Maximize distance between positive and negative hyperplanes with constraints
30:34 The distance between two support vectors in SVM can be calculated using the dot product of the vectors and the unit vector
34:54 The expression for distance calculation and the optimization function in SVM
Crafted by Merlin AI.
Raat me video bnake dark circle bn gye hain, but really thanks for this content. well explained.
All other RUclips videos just explain the basics, but no one explains the maths behind the SVM. But you explained the whole mathematics of SVM. I was stuck on understanding the logic behind the SVM, and even I read many books on ML, but I had not get brief explanation. But your video helps me a lot.
i am a PhD scholar at NIT in Mathematics this video is for actual working of SVM rather than those 10 min video whose aim is just views but the viewer doesn't know.
I am pursuing eco Hons now first year and it is easily understandable what he is trying to teach , most underrated channel
India need teachers like you.
Your explanations are simply best .. if u understand with patience point by point ....u can answer questions on them even in sleep ....thnkx bro
It's commendable how painstakingly you explain every single detail! Wish we had more professors like you.
Thanks a ton for these videos, you're a life saviour..
What an explanation! Beyond words. Thank you for knowledge spreading
Beautifully explained. Stumbled upon this video after a long search. You are better than many of the top ML teachers on YT. Thank you very much :).
Loved your Explanation i am Mtech cse from NIT
Don't have words to thank you sir for this more than amazing explanation ! Tried multiple sources but it became more of rote learning but your video actually made me understand how SVM works ! Thanks a Ton Sir ! :)
i can bet, you can't have a better playlist than this🤲
This guy is damn good
Best explanation I ever seen for such complex topic. Thankyou sir👏
The main goal of SVM is to find the equation of a hyperplane with the maximum margin between positive and negative hyperplanes.
04:22
In machine learning, a decision rule can be derived using the equation w * u + b > 0.
08:44
The equation of the hyperplanes is assumed to be W transpose x + b = 1 for the positive hyperplane and W transpose x + b = -1 for the negative hyperplane.
13:06
The distance calculation involves simplifying equations to x squared and creating positive and negative lines for convenience.
17:28
Multiplying by a factor greater than 1 shrinks the margin, while multiplying by a factor less than 1 expands the margin.
21:50
Maximize distance between positive and negative hyperplanes with constraints
26:12
The distance between two support vectors in SVM can be calculated using the dot product of the vectors and the unit vector
30:34
The expression for distance calculation and the optimization function in SVM
Click to expand
34:54
That explanation with the help of Graph tool is just fabulous.... beginners can easily grasp....Thank you so much 👏
I am having very good learning experience from you
Thanks for your efforts.
Ammmazing guy i just wnated to know about maths of svm to write in my sem exam( b.e) and now i know much more than just that please keep doing tye great work
What , an , explaination ... What an explaination !!! Hat's off boss🙌
Oh Wow Wow wow I Found This Amazing Channel 🔥 🔥
This is the best explanation of SVM....Keep doing good work
data science aspirant needs a teacher like you
Best explanation of SVM I have ever seen till now...Amazing man...keep working
Loved the explanation! Thank you so much
Thanks for giving such a good explaination on this topic on I was struggling to get clearity.It really helped me to get good picture of mathematical concept behind svm.
Next Level , Itne detail meein Kaun batata haiii... you are
while(1) GEM :)
Thank you mere ❤️ bhai 😘😘😘😘😘😘
Sach kahu kai videos me muh maar lis lekin samjh yahi aya i love you
this playlist saved my life
Coming here after MIT openware SVM video and bro let me tell you one thing, you are amazinnnggg
What a explained bro God bless you
It was really very helpful lesson. Thanks for your support.
Best video for svm
It's amazing one 🤩👏... you are requested to recreate one dedicated video for SVM in which you will explain SVM theory as well as one small example
Best explanation of SVM I ever seen ❤🙌 #NitishSir
Feel like clicking the like button million times🙏
Thank You Sir.
take a bow brother.....brilliant explanation
Girls :- Let me do makeup properly i have to take online class
Le boys:- .............🤣
btw very good lecture sir
Great explaination
great explanation Sir
hats off man. this video is amazing
Maja aa gya
fantastic math u explicated.
Nice explanation keep going bro...
superr bro ......Amazing explanation..
Simply explained. ❤
amazing video!
Thanks a lot for this easy explanation :)
Thanks for such nice efforts
Bro your video is extremely good kudos to you. Just improve the sound quality as every thing is hazy while listening that is manageable. But overall awesome❤❤❤
lovely explanation. Thanks alot.
thanks sir 🙏
👍👍👍👍 Best
best best best!!!!
great
Samaj gaya. Ye video pehele kyu nahi dekhi maine
Thank you sir
finished watching
You are a legend bro🎉🎉
thanks it was amazing
Sir can you please make another svm lecture including mathematics of primal and dual formulation and why dual is important
maje aa gaye yaar
pls anyone answer these question
1) the planes pie(+) and pie(-) are decided by the support vectors then how come their equation can be guaranteed to be wx+b=1 and wx+b=-1
2)if the equation are wx+b=1 and wx+b=-1 , the margin(d) we aiming to maximize is basically the distance bw these two planes only, and if we see the distance bw these two planes is 2 which is a constant then how come we can maximize a constant
godly loved it
w bar is what? u bar is what? Cn you please tell? I understood c as the linear distance from origin to the hyperplane.
"Already life mey bahot problems hey, aur pange kyun le rahe hey?"🤣
please tell me how the perpendicular w and the coffiecient line vector w are same ??? how
The so-called real math exaplanation.
you very casually said that projection is dot product, can you please provide any source where its written that dot product is actually projection of one vector over another
Hey isnt the projection formula of w on u is dot prod of w and v divided by magnitude of u?
the equation answer is not 7 it is 10
hello sir why we taking projection of one vector on another??
and how we get that equation 03:54
Thanks a lot SIR
for x1, the equation y(wx+b) should be -1, you have kept it +1, am I missing something ?
I have a doubt, pi+ and pi- are basically hyperplanes passing through the closet points from the hyperplane wx + b = 0(let's say hyperplane p), what if the distance between pi+ and p is not the same as pi- and p, there can be a case in which the distance between the nearest point of one class and the hyperplane p and the distance between the nearest point of the other class and hyperplane p may not be equal.....what to do in this kind of a case? And how do we optimize the distance function?
No, the distances between the hyperplane and margin planes are always equal as the hyperplane is in a way the middle plane of both margin planes
d distance nikalne ke liye W ko unit vector mein kyun convert kiya?
detailed intuition & derivation
why we consider y=wTx+c over y=mx+c
You got a sub :)
thanksssssssssssss
sir your voice is very low at starting of this video , remaining was very helpful
you cant found better explanation of svm on youtube than this video, I guarantee you!!
If you have understood it then can u pls explan why he replaced -C with +b @4.42
👏👏👏👏👏👏
😍😍😍👍👍👍
💕❤️
when replace c with b why to change sign ( - ) to ( + ) ? :(
same question
Please let me know the answer
mjhy khud nhi filhal abi tk pta .
he stored -c in b
But why
♥
Hi sir, As we were trying to achieve hypothesis line in Logistic regression such that it achieves equilibrium among dataPoints. And in SVM also we are doing same thing . Then what is the difference/ how it is solving logistic regression's problem/ or what is the problem in Logistic regression when we have achieved Equilibrium hypothesis line ?
@CampusX
The optimization problem to obtain the plane is different in each
. In svm the loss function has two terms: margin and misclassification components. In logitreg, it's the binary cross entropy. So the method in which we obtain the hyper plane is different
Bud in India we hade nod yed discovered a razor blade!!!
how that -c changed to +b at 4:40 ? I really didn't got it. Can anybody help?
@4:42 You have replaced -C with +b but did not explain the reason. Pls explain it.
perhaps there was a slight mistake it should be
w.u - c = 0.. such that when any point is greater than 0 than it must lie in positive region else negative region... b and c are just contant for positive n negative hyperplane respectively.
when the hyperplane is passing through the origin then only w vector become perpendicular to the decision boundary
but what your teaching???
How can we repalce minus c by positive b
Same Q.
Great video! One question, at 5:00 you made the decision rule vector(w).vector(u) + b >= 0. How did you put 'b' here which is a characteristic of the hyperplane? Thanks for clarifying in advance.
Bismillah
Obviously, you need to know the intercept of the line. Only knowing the vector w will not help because without b, you don't know where the line is clearly situated. With vector w, you know the direction of the line but not it's exact postition. The line could have a gradient of 3, for e.g., but if you don't know where the line cuts the y axis, how can you make your decision?
did you find the answer?
@@nikipatel7602 its similiar to theta0 in linear regression ...exactly what the justcook commented above
ruclips.net/video/yCAlHPDgWtM/видео.html
sir at this point of time you are multiplying equation by some constant but why right side is not changing ????
asli i'd se aao Andrew NG
😂
great