Support Vector Machine (SVM) in 2 minutes
Вставка
- Опубліковано 8 вер 2021
- 2-Minute crash course on Support Vector Machine, one of the simplest and most elegant classification methods in Machine Learning. Unlike neural networks, SVMs can work with very small datasets and are not prone to overfitting.
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This video would not have been possible without the help of Gökçe Dayanıklı. - Наука та технологія
Me watching 2 minute video in 2x speed to understand SVM in 1 minute.. wooah..!
fr🤣🤣🤣
I understood in 2 minutes what i couldn't in two hours. Thank you for such a great video!
Hooray! Thank you very much! :)
This is power of visual.
+1
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Great video!!! Right to the point, no fillers. Thanks a lot!
I don't think, anything could have been much simpler and more fun to learn than this video. Cheers! Can you also make a video of Support vector regression?
Probably the best explanation I found on SVM so far. Great job!
As 2 minute video, we would also expect detailed explanation videos with this clarity and visualizations. Loved your video so much and hoping you will get more views and subscribers!!!
In 2 minutes, you explained to my understanding a concept I've been trying to understand in the last 3months. Thumps up man
HATS OFF FOR THE TIME AND EFFORT PUT IN MAKING THE ANIMATION!!
It literally would take me 3-4 days, maybe more (not working continuously) to achieve that perfect animation.
Many tutorials are needed in visual representations for easy understanding. We are tired of assuming 2D, 3D dimensions. Learning is easy this way, won't forget in life time. GreatWork!
A Simple and effective presentation of SVM. Thanks Man. God bless you.
This may be the most informative 2 minute video I've ever seen. The complexity of the topic to time taken to explain ratio is very high.
Just what I needed damn. I rarely come across quality videos like this nowadays. Keep it up
Tremendous quality-videos! Thank you! Hoping to see your channel grow!
you are crazy this is crazy my whole half of spring semester saved with this video
Wow the way u explained this was top notch… loved it thank you
your 2 minutes of videos can build someone's bright future ♥️💯😃
Such a great demonstration.
Saved a lot of time to understand the important concepts through visualization.
Fantastic, I am glad this was helpful!
You and Ahmad Bazzi are my goto channels
This is the best explanation of the SVM.
Short. Simple. Concise. Thanks Bud!
I'm glad I've randomly stumbled upon your channel ngl, these videos look really well made and are easy to get a grasp of
Thanks, I appreciate that. That was the goal of this videos.
Wow what an amazing video, i understood svm in 2 minutes, which I didn't watching other 15 minutes tutorial
you aresuch a great they thought same thing for 2 months which i cant understand but today is my exam your 2mins made my day
Wow this was unbelievably concise. Thank you so much!
This is the best video I've ever seen, this is one of the problems of online courses that they go on and on for hours explaining few important things. For a person like me who has small attention span this works the best.
Hey there, thanks a lot of your encouraging feedback, and stay tuned for more videos like this!
Well explained in such short duration.
Thanks!
A comment to support this incredible video. Well explained and gorgeous animations! Thank you very! much.
Thank you very much!
I'm liking, sharing and subscribing. This is too wonderful.
hats off for brevity and clarity
Very good animated explanation! Thanks !
Amazing video summary. Wish my lecturers would just show this at the beginning, so everyone immediately understands it, and the rest of of the lecture can just be about the math and implementation.
Thanks for the extremely nice comment :)
Nicely Explained. Plz make full course on Machine Learning 🙏
This is such a nice explanation. THank you for this.
This was incredibly informative, thanks!
Absolutely amazing video! Subscribed.
THANK YOU SOO MUCH SO WORIED ABOUT EXAM AND YOU MADE IT CLEAR
couldn't be explained better, thanks
likes and subbed
Now you may not have many subs, but someone with this quality of content and editing is going to go far, the videos are incredible, keep it up !!
New sub, have a beautiful day!!
Awesome!!! I'm glad you enjoyed the video. :)
The content is insane , a huge explanation of SVM ,I didn’t expect that !
Do you plan to talk about the different types of projection for the kernel tricks?
Awesome!!! I'm glad the video was helpful. :) Kernel tricks are fascinating, I hope I can find the time to talk about them at some point.
You deserve ❤️ Great explanation 🔥🎉
Very well explained.... Appreciative ❤
this is excellent. please do more machine learning videos!
lucky me that you shared it a month ago and I've came across the topic today
Amazing simply amazing you just helped me for final cl project presentation what I didn’t understand all semester fantastic job
Great to hear, thanks for writing this very nice comment.
Nice and quick explanation man, tanks!
Perfect explanation thank you 🙏🏼
I am happy to be part of this
Thank you so much I really like your videos.
Please keep posting.
Thank you, I will
the best 2 minutes utilization
Thank you so much, loves from Afghanistan
Just got yourself a subscriber, keep it up.
Very Informativ. A big thumbs up!
BRILLIANT ! PERFECTLY PRESENTED
Thank you!
Amazing content. Thank you for your efforts! :)
My pleasure! :-)
such a fancy name for a hyperplane thanks for the heads up tough
Your videos are great! Very clear and with interesting topics. I’m wondering if you have any experience with trajectory optimization or optimal control?
Thank you, I appreciate that! Optimal control is on the agenda :-)
Great visual explanation🙂
Nice explanation and the content was great. Keep it up bro.
Thanks a ton
really well explained and visualized! thank you very much for sharing! :) subbed!
Awesome, thank you!
Great editing skills 👍🏻
Great job. Nice viz and explanation
Fantastic video! Thank you for sharing
My pleasure :)
wow
new subscriber here! subscribed from both my account to make sure i do not miss your content, super educational and got me very engaged! thank you so much
Great video, Thank you
Pretty Cool Stuff. Make more of these
Well that was a great Video! Big thanks
great explanation!
Stellar video!
WHAT A GREAT CHANNEL !
Thanks a lot Abd!
the best explanation
Great topics and great explanations
Glad you liked it
Amazing Video !!!
this was so helpful man
Amazing video!
Could you make another one about SVM use for anomaly detection in a non supervised manner? Thanks! Great video!!
Hey Visually Explained! Great videos. Love your channel. Subscribed and liked 100%. Can you please also do a video on Kernals for SVM???
wow amazing ...thanks mate
Very good in short time visually explained
Thanks and welcome :-)
great video, thank you!
Good explanation!
Wow, I'm so glad I found this channel. Can SVMs b used for multiclass classification?
Yes! One way to do it if you have n classes is to train n different classifiers, where classifier "i" tells you whether your input is in class "i" or not.
Basically every binary classifier can be used for multiclass classification tasks .
But you probably know that yet.
Love your explanation
Thank you!
Great brief thanks a lot ♥
Wow! This is great
Awesome content and video edition, thank you so much. Do you have any advice to produce such kind of graphics and animation ?
best contribution and wonderful animation. how you make such great animations?
Nice video! thanks!!
Thank you!! Genius
SO GOOOD!!!!
Great video very helpful
Great Video
Dude, this is amazing! How did you make these animations? They seem sort of like Primer's, so Unity?
Thanks for the compliment! I don’t use Unity, but Blender3D. It’s open source and “script-able” in python, it’s really awesome.
@@VisuallyExplained Teach me the way
Fantastic video! Very well explained 👏
Glad you enjoyed it!
Thanks! This is very nice because it's visualized in an intuitive way!
Three questions: Why is there a -2 in the norm || w ||^(-2 ) 1:12? And isn't w the slope of the hyper-plane? If so, why do you want to maximize a slope?
Great questions!
1. There is a "-2" because we want to maximize 1/||w||^2 (the inverse of the square of the norm of w)
2. W can indeed be thought of as the slope of the hyperplane.
3. We actually want to maximize 1/||w||^2, which is equivalent to minimizing ||w||. The reason we want to do that is because if you can consider the two hyperplanes:
wx+b=1
and
wx+b=-1,
which are denoted by a dashed line in 0:50, the distance between them, or the "margin", is equal to 2/||w||. And we want this margin to be as large possible to separate the "+1" and "-1" points as cleanly as possible. See for example page 10 of the link below
web.mit.edu/6.034/wwwbob/svm-notes-long-08.pdf
please please please i request on behalf of millions that you kindly add other ML algos. It is so beautiful to understand
I get in 2 minutes more than a full course 👌
Awesome!!!! I'm so glad to hear my videos are helpful and good luck with your courses.
AWESOME!
nice thank you more from that
00:01 SVMs are elegant and effective for classification tasks.
00:18 SVMs classify points in n-dimensional space using features
00:34 SVM finds the hyperplane that best separates two categories.
00:52 SVM requires a labeled training set for supervised learning.
01:08 SVM maximizes margin with category points on correct side
01:25 SVMs are easy to understand, implement, use, and interpret with effective performance on small training data
01:44 Support Vector Machine uses clever techniques for nonlinear data
02:03 SVM can be used for face detection, spam filter training, and text recognition.
Very good!