A dive into the all-powerful gradient boosting method! My Patreon : www.patreon.co...
Пікірлер: 84
@ew63922 жыл бұрын
Man I've discovered your channel and am watching your videos non-stop. No matter which topic, it is ALL as if a stream of light shines and makes it all understandable. You've got a gift.
@zhenwang5872 Жыл бұрын
Agreed! You've got a gift to shine the light over topics.
@sELFhATINGiNDIAN4 ай бұрын
No
@soroushesfahanian56253 ай бұрын
The last part of 'Why does it work?' made all the difference.
@javierperezvargas91323 ай бұрын
totally agree
@shnibbydwhale3 жыл бұрын
You always make your content so easy to understand. Just the right amount of math mixed with simple examples that clearly illustrate the main ideas of whatever topic you are talking about. Keep up the great work!
@KameshwarChoppella3 ай бұрын
Non math person here and even i could understand this tutorial. Probably have to see it a couple more times because I'm a bit slow in my 40s now. But you really have a gift. Keep up the good work.
@mrirror22773 жыл бұрын
Hey thanks a lot, was literally just searching about Gradient Boosting today and your explanations have always been great. Good pacing and explanations even with some math involved.
@marcosrodriguez24962 жыл бұрын
your channel is criminally underrated. Just one question. You mentioned using linear weak learners, i.e. f(x) is a linear function of x. In this case how would you ever get anything other than a linear function after any number of iterations? at the end of the day, you are just adding multiple linear functions. it seems this whole procedure would only make sense, if you pick a nonlinear weak learner.
@jiangjason5432 Жыл бұрын
Great video! A bonus for using squared error loss (which is commonly used) as the loss function for regression problems: the gradient of squared error loss is just the residual! So each weak learner is essentially trained on the previous residual, which makes sense intuitively. (I think that's why each gradient is called "r"?)
@samirkhan619514 күн бұрын
Yeah, squared error is easily differentiable compared to others like root squared error, and is not dependent upon number of observation like mean squared error or root mean squared error does , if you want gradient exactly equal to residual , you can choose to take (1/2)(squares error) as loss function.
@nikhildharap45142 жыл бұрын
you are awesome man! I just love coming back to your videos every time. they are just the right length, and the perfect depth.. Kudos!
@АннаПомыткина-и8ш2 жыл бұрын
Your videos on data science are awesome! They help me to prepare for my university exam a lot. Thank you very much!
@MiK98Mx Жыл бұрын
incredible video, you make understandable a really hard concept. Keep teaching like this and big things will come!
@alicedennieau5459 Жыл бұрын
Completely agree, you are changing our lives! Cheers!
@hameddadgour5 ай бұрын
This is a fantastic video. Thank you for sharing!
@ritvikmath5 ай бұрын
Glad you enjoyed it!
@honeyBadger5823 жыл бұрын
Great video as always! I would love If you could build on that video and talk about XGBoost and math behind it next!
@pgbpro203 жыл бұрын
I worked on this 5(?) years ago, but needed a reminder - thanks!
@arjungoud34502 жыл бұрын
Man U r the 5th person, none has explained as simple and clear as you, thanks a ton
@MiladDana-b7h2 ай бұрын
that was very clear and useful, thank you
@ToughLuck808 Жыл бұрын
Unbelievable variety of topics in this channel! What is your daily job? You have an amazing amount of knowledge
@luismikalim2535 Жыл бұрын
Thanks for the effort u put in to help ur watchers understand, it really helped me understand the concept behind gradient descent!
@rajrehman98122 жыл бұрын
Can mathematics behind ML be less dreadful and more fun? Well yes, if we have a tutor like him... amazing explanation ❤️
@jakobforslin63012 жыл бұрын
You're an amazing teacher, thanks a lot from Sweden!
@Andres1860003 жыл бұрын
Thanks for the video, also really like the whiteboard format
@Matt_Kumar2 жыл бұрын
Any chance your interested in doing a video on EM algorithm intro with a toy example? Love your videos please keep them coming!
@Sanatos98 Жыл бұрын
Pls don't stop making these videos
@adityamohan73727 ай бұрын
Finally understood it really well, thanks!
@jonerikkemiwarghed76522 жыл бұрын
You are doing a great job, really enjoying your videos.
@Halo-uz9nd3 жыл бұрын
Phenomenal. Thank you again for making these videos
@Ranshin0773 жыл бұрын
Very awesome, thanks for the explanation 👍
@GodeyAmp4 ай бұрын
Great video brother.
@markus_park Жыл бұрын
Thank you so much! You just blew my mind
@ritvikmath Жыл бұрын
You're very welcome!
@ИльдарАлтынбаев-г1ь4 ай бұрын
Man, you are amazing!
@jeroenritmeester732 жыл бұрын
In words, is it correct to phrase Gradient Boosting as being multiple regression models combined, where each subsequent model aims to correct the error that the previous models couldn't account for?
@domr.26942 жыл бұрын
Thank you for this good explanation.
@joachimheirbrant1559 Жыл бұрын
thanks man you explain it so much better than my uni professor :)
@ritvikmath Жыл бұрын
Glad to hear that!
@bassoonatic7773 жыл бұрын
Excellently explained. I was just reviewing this and was very helpful to see how someone else think through this.
@dialup56k Жыл бұрын
well done - gee there is something to be said about a good explanation and a whiteboard. Fantastic explanation.
@ritvikmath Жыл бұрын
Thanks!
@garrettosborne43642 жыл бұрын
Best boosting definition yet.
@ganzmit28 күн бұрын
nice video series
@EW-mb1ih2 жыл бұрын
let's talk about the first word in gradient boosting..... boosting :D Nice video as always
@kaustabhchakraborty4721 Жыл бұрын
Just asking that is the concept of gradient Boosting similar to Taylor Series functions. Each term is not very good at predicting the function but as u add more functions(terms), the approximation to the function gets better.
@benjaminwilson1345 Жыл бұрын
Perfect, really well done!
@ritvikmath Жыл бұрын
Thanks!
@zAngus5 ай бұрын
Thumbs up for the pen catch recovery at the start.
@ritvikmath5 ай бұрын
😂
@Sam-uy1db8 ай бұрын
So so well explained
@rickharold78843 жыл бұрын
Hmmmm v interesting. Something to think about. Thx
@estebanortega38952 жыл бұрын
Amazing video. Thanks.
@ianclark67303 жыл бұрын
Love the videos! Great topic
@sophia17965 Жыл бұрын
Thanks! great videos.
@emirhandemir38722 ай бұрын
The first time I watched this video, I understood shit! Now the second time, I studied the subject and learn more :), it is much more clear now :)
@tobiasfan54072 жыл бұрын
You're the man. Thank you!
@lashlarue7924 Жыл бұрын
Bro, it's late AF and I'm not gonna lie, I'm passing out now, but I'mma DEFINITELY catch this shit tomorrow. 👍
@ritvikmath Жыл бұрын
😂 come back anytime
@lashlarue79244 ай бұрын
@@ritvikmathWell, it's been a year, but I came back! 😂
@user-xi5by4gr7k3 жыл бұрын
Great video! Never seen gradient descent used with the derivative of the loss function with respect to the prediction. Not sure if I understand it 100% but If the gradient were, for example, -1 for ri, would the subsequent weak learner fit a model to -1? Or would the new weak learner fit a model to (old pred -(Learning Rate * gradient))? Would love to see a simple example worked out for 1 or 2 iterations if possible. Thank you! :)
@xmanxman152711 ай бұрын
Isn't gradient the partial derivative with respect to feature(xi), not with respect to the prediction(y^)?
@sohailhosseini2266 Жыл бұрын
Thanks for sharing!
@mitsuinormal9 ай бұрын
Yeiii you are the best !!
@m.badreddine94668 ай бұрын
move on so I can get screenshot 😂. brilliant explanation ,well done
@chiemekachinaka523618 күн бұрын
Thanks man
@7vrda7 Жыл бұрын
great vid!
@chocolateymenta2 жыл бұрын
great video
@arjungoud34502 жыл бұрын
Can you please make a video on XGBoost and its advantages by comparing. Thank you.
@jamolbahromov44402 жыл бұрын
Hi, thank you for this informative video. I have some problem understanding the graph at 5:27. How do you map out the curve on the graph if you have a single pair of prediction and loss function values. do you create some mesh out of the give pair?
@gayathrigirishnair7405 Жыл бұрын
Come to think of it, concepts from gradient boosting apply perfectly to less mathematical aspects of life too. Just take a tiny step in the right direction and repeat!
@ritvikmath Жыл бұрын
yes love when math reflects life!
@VictorianoOchoa Жыл бұрын
are the initial weak learners randomly selected? If so, can this initial random selection be optimized?
@adinsolomon16263 жыл бұрын
Learners together strong
@saravankumargowthamv9338 Жыл бұрын
Very good content but then it would be great if you can stay at the corner allowing us to have a look at the board for us to understand otherwise great session
@ritvikmath Жыл бұрын
Thanks for the suggestion !
@ashutoshpanigrahy7326 Жыл бұрын
after 4 hrs. of searching in vain, this has truly proven to be a savior!
@regularviewer16822 жыл бұрын
Honestly, StatQuest has a much better way of explaining this. First he explains the logic by means of an example and then he explains the algebra afterwards. I'd recommend his videos on gradient boosting for anyone who didn't understand this. Without having seen his videos on it I would have been unable to understand the algebra.
@BettyBarry-u2m12 күн бұрын
Martinez Barbara Moore Edward Thompson Maria
@SimplyAndy Жыл бұрын
Ripped...
@sharmakartikeya3 жыл бұрын
Hello Ritvik, are you on LinkedIn? Would love to connect with you!