Perceptron

[@abhivaryakumar3107]

Mans explaining with 3 markers and a chopstick and its clearer than any professor could explain with slides and computers and simulations Youre an amazing teacher Thank you so much

[@joker345172]

ikr this guy is too good 😭😭 Universities hate him 😭😭😭

[@AstonishingStudios]

Quit reading the comments, boys, because this video will be all you need if you just focus on it. Great explanation!

[@saisrisai9649]

I m a grl

[@gouthamks1274]

@@saisrisai9649 😂 nice

[@wyattaxton4085]

i dont mean to be off topic but does any of you know of a trick to get back into an Instagram account..? I was stupid lost my login password. I love any tricks you can give me.

[@stephenkelvin1220]

@Wyatt Axton instablaster =)

[@wyattaxton4085]

@Stephen Kelvin i really appreciate your reply. I got to the site through google and Im trying it out now. Seems to take a while so I will reply here later when my account password hopefully is recovered.

[@jestivalv]

Incredible how an explanation with a piece of paper can be more effective than any other digital methods. Thanks

[@ritvikmath]

Glad it was helpful!

[@Petershd138]

I confess, I didn't understand any single word of explanations from my MIT professor about perceptron. How ever, after I saw this video and understood clearly what the idea is. Thanks.

[@sesmuel2593]

How are you at MIT, I cannot even find a way so sign up on the website.

[@muzspamz]

@@sesmuel2593 you gotta use the Perceptron to get in

[@mayankdubey9027]

I’ve been trying to understand perceptron for a couple days now. Nothing really clicked until this video. 10/10 explanation, you are better than majority of the professors and teachers out there.

[@Amar-hl6pu]

Same ! Been trying for 2 days and most videos don't provide a detailed mathematical explanation and just throw the formulas at you. This is by far the best explanation of a Perceptron on KZbin.

[@shelleyzhang5910]

You're really a talented teacher! I love the way you walk us through such abstract concepts. I was struggling with my HW for a whole day until I saw your videos. Keep producing more brilliant content! Subscribed!

[@alapparate8768]

the only perceptron video I needed, btw I have exams tomorrow

[@alimensah1153]

The same here 😂😂

[@skyboat345]

Me too lmao

[@alapparate8768]

@@skyboat345 all the best 😊

[@skyboat345]

@@alapparate8768 Haha thanks man!

[@Kosmaty205]

And how it went? :D

[@AlexLaird7]

Amazing clear, and concise explanation! Thank you so much, I'm trying to program a perceptron neural network in Go and this really helps

[@ticoticox3000]

Isnt it gradient descent?

[@maska59]

Great video, you pretty much have explained the whole perceptron problem in 15 minutes while I was trying to figure it out after taking 4 hours of uni classes.

[@lastairbender_883]

07:29 By far the best use of a disposable chopstick in a ML video. Well done!

[@senzmaki]

chopstick Meta

[@fnyaung]

wow great video! Way easier than how my professor explains it! My prof uses terminologies, which he doesn't explain, and it gets confusing at times.

[@ritvikmath]

Glad it helped!

[@TheDominomi]

This video just solved one assignment I had from university. Good explanation, thanks

[@phil97n]

First time watching something from the author. This is a fabulous explanation, thank you.

[@sajulali]

Wow...never had anybody explain things so clear...just loved and learnt a lot about perceptron!! Thank you!!

[@charlyn.1219]

Thank you for the video! It is well explained. Some lecturers just have a passion for making easy things difficult.

[@ritvikmath]

You are welcome!

[@shakirasunshinez]

i wish my professor could explain this. idk what she does wasting hours of our time every Tuesday. i leave knowing less than this 13 minute video

[@Randomkitten7]

EXACTLY

[@Canonall]

You either are simply lying or you are massively overestimating what you are able to do after watching this video. In University you learn this concepts in a formal, rigorous way. You learn the proofs, definitions, etc. Pretending this is in anyway a substitute for any university lesson is just plain dumb. It's a complement, for sure.

[@smsm8686]

such a clear explanation! you're a natural at teaching and machine learning. thanks so much

[@ritvikmath]

You're very welcome!

[@shankarc6321]

This is amazing, I've been scratching my head with lecture videos and notes.. going nuts.. but this explanation is simply super... I have been looking for this to get the concept and fundamentals. Love it. Please keep posting more and more..

[@mjt_00]

REALLY GOOD VIDEO. Thank you so much, why can't our teachers at university explain like this.

[@maxton550]

Thank you man , my machine learning class is a mess .

[@suvrotica]

Just amazing. Please keep up the good work, it is this kind of clarity that makes people like topics like this.

[@dicember92]

All I have to say is a huge THANKS. I was getting mad because I thought that the weights vector was sized n x 1. And most importantly I didn't get that it would consider only the misclassified points. Now it really makes sense.

[@nicolaspazmino6477]

Thank you for taking the time to make these videos and share your knowledge, your other videos deserve more views.

[@ukulelevillain4170]

i literally knew next to nothing about ai learning and you tought it in 10 minutes. congrats to you.

[@TheLipe31]

Bro in 13 minutes made me understand perceptron algorithm better then my teacher with 2 phds in maching learning and computer science

[@simond9076]

Excellent video, clear. Props to the use of a chopstick and the little treat at the end also.

[@Hamromerochannel]

This is one of the best best best channel for learning ML

[@ritvikmath]

Thanks!

[@Canaano_0]

one of the best ml video

[@scorpio19771111]

Really good video! Is no one going to to talk about how he drew a line twice with a marker using a stick, and yet was perfectly spot on??

[@diamondcutterandf598]

8:49 how do you define upper and lower? If the line is mirrored, is the upper region still the 'right' side of the line, or the 'left' side

[@dumb_as_rocks]

most aesthetic handwriting i've ever seen

[@tanmaypardhi9848]

Exactly what I was looking for!! Explained shortly and precisely.

[@1998Valerio]

Well explained and love the usage of chopstick as a ruler.

[@vanajaparamesh4752]

Very useful for my exams..... Thanks a lot sir

[@Ejun0]

Great video! The only thing I would add is that the activation function is the step function which is the d in the video.

[@zakozakaria]

converting data to polar coordinates is just beautiful

[@frankyin8509]

This ten minutes video way much better than the legacy slides in my class 🎉 otherwise i wouldn't fail the exam 😅

[@ritvikmath]

Glad it helped!

[@AzAzMusic]

bro its crazy how ive been learning this in class for the last 2 weeks and I didn't understood them until now

[@austinoquinn815]

This is the best video ive seen on this topic. Thanks!

[@drewlehe3763]

Still my favorite math/stats channel

[@ritvikmath]

Thank you!

[@vanshikajain4800]

Crisp n clear explanation, thanks for the extension part, really needed to know that!

[@Jaschka15]

best and most simple explanation ever. thanks so much

[@carlhelin8744]

random guy with a chopstick and marker better than most professors at my uni

[@aryamananand100]

Hi, amazing video! Quick question: Is the learning rate represented by the Greek letter "eta" or "nu"? Because your drawing ressembles "eta", and you wrote "nu" beside it. Thanks

[@sawubonalanguagelearning]

i think he meant to say 'eta' because the learning rate is either represented by eta η or by alpha α. Nu (ν) is a different letter altogether

[@krishanudebnath1959]

this kind of voice is how I know its gonna be a good one

[@Thelee4music]

at 6:15, from where this equation derived X2 > -2X1

[@rackzz7084]

it's clearly wrong and should be x2 > -1/5 x1

[@satyapratik]

X1+0.5(X2)>0 0.5(X2) > -X1 X2> -2(X1) You should re do your elementary schooling.

[@rackzz7084]

@@satyapratik dude the 0.5 in the video looks like a 5

[@gugankathiresan4721]

@@rackzz7084 ikr

[@buyanimhlongo2414]

You're correct his answer is wrong,After a Dot Product the answer is :X1+0.5X2

[@dianaayt]

8:47 but what about when you have more than 1 moitake and some should be in the upper and some in the lower?

[@if8172]

Need teachers like this guy teaching like we're all bloody five instead of my lecturer who decides to fill the first half of the powerpoint slides with biological neuronal networks ffs

[@dystopiaproductions9869]

Took me a while to understand which greek letter "nu" is. It's the greek letter "eta". You may see in some programs that the learning rate is called eta instead.

[@andrejalbrecht]

Great explanation on the good old paper. Thank you!

[@Cobyboss12345]

you are so amazing you are reading 3 years into the future

[@joker345172]

Bro is out there with a couple of markers, a piece of paper, using a disposable chopstick as a ruler and explains this better than entire universities 😭😭😭

[@manuelberrueta]

Excellent job. Super clear on how it works!

[@marcfruchtman9473]

Really excellent video. Especially the tip on the last section re: data that has a circular / polar coordinate separation. (Not sure what the technical term for that would be) Thanks for the video!

[@dennisgavrilenko]

Not the hero we deserved, but the hero we needed

[@hbeing3]

thank you! Well explained. A small question about the symbol of learning rate. Is it called 'Eta'? 'Nu' is the v shape one?

[@ritvikmath]

yup you're right, I clearly need to brush up on my Greek alphabet

[@yyaa2539]

Thanks for the video. Two remarks: 1. It is not clear if the algorithm converges to a solution. 2. mistake at 10:15-10:34: the new line found by the algorithm does not pass throughout the origin...

[@erin1961]

explainable and helpful, easy understanding! THanks a lot!!

[@ritvikmath]

Glad it was helpful!

[@jpdebhevga]

Really nice explanation. You are an awesome teacher!

[@adama7654]

Very good video. The learning rate is the greek letter "eta", the letter "nu" looks more like a v.

[@salonikothari7265]

clear and super intuitive illustrations !

[@rakshashet7813]

Really great explanation! Loved it!😍

[@nbavc]

Amazing video, very clear and insightful video. Thanks!

[@davit20_02]

Thank you for your time and efforts, clearly described everything

[@SuraJGhimire]

thank you for explaining it so well.

[@ritvikmath]

Glad it was helpful!

[@gerben880]

yo this η is an eta, not a nu lol

[@ritvikmath]

Oops haha

[@mikepeyton6720]

Okay Sig Nu pledge

[@carlmachaalany4328]

something about the chopstick makes me understand your explanation better

[@marks7384]

This is a great video, thank you so much for sharing your knowledge.

[@samerkeyrouz3160]

Great video straight to the point thanks

[@ΣτέφανοςΒκς]

It helped me my friend, really appreciate that. :)

[@krithikavenkatesan2123]

Great video! I'd like to know how the addition of another element to the number of dimensions, gives us a constant number? How does just the no. of Dimensions refuse to provide a constant number?

[@ericsims3368]

Super helpful explanation. Thank you! Is the updating the same as back propagation, or is that only with multiple layers?

[@megan9547]

Just wanted to say thank you! Great video.

[@ritvikmath]

Thank you!

[@farooq36901]

excellent explanation Sir.

[@martincarbonell7032]

My dream teacher! thank you

[@ritvikmath]

Any time!

[@srw6772]

This is exceptionally well done.

[@andrempunkt]

0*1 + 1*x1 + 5*x2 > 0 1*x1 + 5*x2 > 0 x2 > - 1*x1 x2 > -(1*x1)/5 x2 > -0.2*x1 Is that corrct or did I make a mistake?

[@mohitnagpal]

Simple and clear. Loved it...

[@shahabmalayeri3315]

Wonderful introduction. Thanks.

[@spookyalaska]

Thank you for the great explanation.

[@UnTizioACaso1]

Thank you, very clear and helpful!

[@ritvikmath]

You are welcome!

[@AndrewT]

Is the change of coordinates from cartesian to polar related to the so called 'kernel trick'?

[@guirck]

This helped a lot. Thank you!

[@crabjuice47]

Maybe not chose one of the symbols on the graph as a cross so it doesn't further confuse someone with all the x. And x1 and x2 are harder to distinguish then x and y.

[@user-or7ji5hv8y]

this is a great conceptual explanation

[@adamtan1671]

Great use of a chopstick!

[@ritvikmath]

Thank you 😋

[@H1K8T95]

Ritvik, I saw the thumbnail and title and thought I was heading into a Bill Wurtz video 😂

[@ian-haggerty]

[@easybeeee]

fantastic explanation, thank you

[@shacharh5470]

Would polar coordinates work if the circle isn't centered around 0,0?

[@saulesha123]

1 million thanks

[@blauwecavia1583]

So what if 2 points are incorrectly seperated? Do I just pick one at random to update?

[@atouloupas]

Great video! Small correction, η (the learning rate) is the letter "eta", not "nu"

[@stevengusenius7333]

Thanks, i haven't seen this one before. If there are multiple lines that will separate the classes, is the line selected by perceptron optimal in anyway, or does it simply stop when any viable solution is found? If there is overlap between the classes, will this still work? Assuming there is no one line that separates the classes with 100% accuracy, will iterating get you a "better" solution?

[@edwardzhang1297]

bro didn't even have a ruler and had to use a chopstick. In all seriousness great video

[@ritvikmath]

Thanks 😅

[@alitahsili]

What if we had two or more misclassified points? How would the w(i) be updated?

[@TheLiadt]

Well explained but what do I do if there are 2 wrong dots? Do I sum the changes I make to W'?

[@toshal22]

You assigned the omega values for the question but how did you initially assign their values and why did you assign them as 0,1,.5 why not something else ?

[@tharteon1866]

whats the answer to this?

[@skndshaum]

How you get x1 = 2 ; and x2 = -2 ?

[@etion1999]

8:35

[@LuyoloLuthoMpendulo]

9:06 how did arrive at the x1 and x2 values?