The fact this video is free is incredible

@umarjamilai

29 күн бұрын

You're welcome 🤗

Your videos are literally the only ones with 1hr+ I would ever watch on KZread. Keep going mate, extremely high quality content 👏🏽👏🏽

You're on a mission to make the best and friendliest content to consume deep learning algorithms and I am all in for it.

Thanks a lot for making this accessible for people outside the field, for which reading and understanding these papers is quite tough. Thanks to you I'm able to stay slightly more up to date with the crazy quick developments in ML!

Best explanations of splines i have seen. Legit 100%

Incredibly clear explanations, the flow of the video is also really smooth. It’s almost like you’re telling a story. Please keep making content!!

Wow this was a super clear an on-point explanation. Thank you, Umar.

Amazing content, thanks! I'm very excited about the continual learning properties of these networks.

Clearly explained and very valuable content as always Umar. Thank you!

The intro of a basic linked up linear layers was so well done and really makes this introduction friendly!

Thanks Umar for such a wonderful tutorial! I've been eyeing this paper for a while!

Your videos help me (a grad student) really understand difficult, often abstract concepts. Thank you so much... I'll always support your stuff!

Very clear, well explained, top notch!

This is life changing, in my opinion. Thank you for the efforts on the videos!

awesome, easy to follow even person dont know anything :)

One of the best math videos I’ve watched on KZread

Fantastic explanation!

This is really great! Power to you!!🚀

Thanks for including prerequisites

Thanks for the crystal clear explaination!!

Hello Umar, this video is my best birthday gift I have ever received, thanks a lot :)

that is very useful, informative and interesting! Thanks a lot!

Sir, you are great..💙💙

Extremely clear explanation and content here! Very helpful. I am happy that you came from PoliMI as well :) keep it up!

Thankyou Jamil, what a cool video

Hats off, what an awesome video!!!

Thank you for such great and detailed explanation.

Thank you so so much for this amazing content.

What funny, is that i predicted your next video will be on KAN, after i see you in github. I WILL WATCH THIS VIDEO, AS I FEEL THIS WILL BE THE FUTURE OF NEUR NETWORK, THANK YOU FOR YOUR WORK AND CONTENT ❤

High quality explanations.. Thanks.

Your explanations are the best, thank you so much😘🤗

You are savior, without you mortals like me would be lost in the darkness!!!

Thank you for making this video!

Amazing video! Thanks a lot !

I saw this paper on papers with code, and thought to myself I wonder if Umar Jamil will cover this. Thanks for your effort and videos!

I think KAN will be the catalist of a significant tipping point in science. I want to apply this to power system grids and replace existing dynamic models with ones made from PMU data using KAN

Ho appena letto la piccola bio del tuo canale, spero di non essere offensivo dicendo che adesso capisco perché il tuo ottimo inglese mi sembrasse comunque molto familiare. Ad ogni modo ti ringrazio enormemente per il tuo contributo hai spiegato tutta la teoria in un modo, a mio avviso, estremamente chiaro e soprattutto coinvolgente. Ti prego continua così, di nuovo un enorme grazie e complimenti per il tuo contributo alla scienza

@umarjamilai

22 күн бұрын

Grazie a te per aver visitato il mio canale! Spero di pubblicare più spesso, anche se per fare contenuti di qualità ci vogliono settimane di studio e preparazione. In ogni caso, spero di rivederti presto! Buon weekend

@luigigiordanoorsini5980

22 күн бұрын

@@umarjamilai Avevi già guadagnato un iscritto adesso hai guadagnato un fan. Ahahahahah

crazy that it took me an hr video to understand that its the (control points) being trained on the spline graph vs weights with MLPs and CNNs, thank you!

Thank-you so much for explaining the paper, it is so easy to understand now, btw can you also make a hands on video with the kan package developed by mit which is based off pytorch.

Good video, quality content.

awesome explanation

Phenomenal! Thank you :)

You’re fantastic, mate.

Great Content !!

Amazingg explanation !

This is awesome!

Cant wait to watch this, saved! Will comment again when i actually watch it..😅

i loved it sir .

bruh so good. Keep it up!

brilliant video!

Please do post more ! please do more videos !

An implementation video will be awesome

Excellent video, thanks! At the end, I _really_ wanted to see an illustration of the relatively "non-local" adaptation of MLP weights. Can that be found somewhere?

Hey @Umar, great content as always. Looking forward to a KAN implementation video from scratch. Also I think in 31:01 there is a minor language mistake. I think it will be for using a quadratic Bspline curve rather than quadratic Bezier curve

Having a such good teacher is so adorable, i wish i could be your students.

@umarjamilai

27 күн бұрын

哪里哪里啊，谢谢你的赞成！

@seelowst

27 күн бұрын

@@umarjamilai 太棒了，您还会中文👍

@umarjamilai

27 күн бұрын

@@seelowst 我就是刚刚从中国来的，在中国主了4年了，现在回欧洲了。

@seelowst

27 күн бұрын

@@umarjamilai 我从没离开过我的城市，我希望像您一样👍

awesome👍

this video is so amazing！！！！！！！

THANK YOU

awesome bro.

thanks Umar. Very nice explanation. Just 2 questions : 1 - Does it mean we can specify different knots per edge? 2 - I am not understanding how the backpropagation will work. Let's say we calculate the gradient from h1. It will update phi 1,1 and phi 1,2 but how the learning process will impact the knots to the desired value?

thank you

Umar bhai you the great

Thanks man. Next xLSTM please.

amazing

Thanks!

刚好期盼这个!

@umarjamilai

29 күн бұрын

期待你的评价😇

@daleanfer7449

29 күн бұрын

❤很好的内容，有考虑做inverse rl的内容吗❤

Thanks

Great explanation. What app do you use to create slides ?

@umarjamilai

27 күн бұрын

PowerPoint + a lot a lot a lot a lot a lot of patience.

Valeu!

Hi, can you please make a video on multimodal LLMs, fine tuning it for custom dataset...

Thank you for the great video! Can you (or anyone) help understand why you need to introduce the basis functions b(x) in the residual activation functions?

Could you please next explain multi modal llms, techniques like Llava, llava plus, llava next?

@Patrick-wn6uj

29 күн бұрын

I waiting for that day too

Wow! 🙏

Time to implement it

One thing I didn’t catch: how are the functions tuned? If each function consists of points in space and we move around the points to move the B spline, how do we decide to move the points? Doesn’t seem like backprop would work in the same way.

@umarjamilai

28 күн бұрын

The same way we move weights for MLPs: we calculate the gradient of the loss function w.r.t the parameters of these learnable functions and change them in the opposite direction of the gradient. This is how you reduce the loss. We are still doing backpropagation, so nothing changed on that front compared to MLPs.

There are continuous but indiferable points in the spline, right? What are you going to do?

Sir I have been a huge fan of your videos and have watched all of them . I am currently in my second year BTech and really passionate about learning ml sir if possible can work under you I don’t want any certificate or anything just want to see observe and learn

fwiw I took a MLP solution for MNIST, substituted KAN for the MLP layers and no matter what I did (adding dimensions etc) it couldn't solve it. My intuition is that KANs only work well for approximating linear-ish functions, not irregular, highly discontinuous ones like image classification would need. But perhaps I just screwed it up :D

Please explain DSPy

can you make tutorial video on model like Perplexity that use website live search

In search of gold i found a diamond

Please add a payment option

@umarjamilai

29 күн бұрын

Your love and support is enough! Have a great weekend!

@Patrick-wn6uj

29 күн бұрын

@@umarjamilaiJust woow

Your explenations are great. I think though, you should take breaks to blow your nose maybe, because you were sniffing a lot. It will make you videos more enjoyable.

Interesting

Thanks!

Thanks