I like how more and more people are adopting 3b1b's style. Makes the content much better and easier to understand. This slowly converts a lot of the more complicated topics into easy-to-digest modules.

@Artaxerxes.

2 жыл бұрын

It literally uses manim

@tomerzilbershtein849

2 жыл бұрын

3B1B’s creator Grant Sanderson created an animation library for himself to use to make videos. People forked that library (made a copy of it) and now there is a community supported version of it for creators, while he continues to use his own ( as well as the community one). Pretty cool stuff!

@atotoole21

Жыл бұрын

@@Artaxerxes. Nice! I didn't know about manim or that 3B1B's animation technic was python based. I assumed it was done by hand using Illustrator or something.

@umbraemilitos

10 ай бұрын

Yes, though I don't think 3B1B wants their videos to be a template to copy. I think he's happy to inspire, but doesn't think that his Manim program is the right tool for most cases. He released a video explaining the SOME criteria, and it allows for lots of creative expression in teaching.

@andreypopov6166

Ай бұрын

3b1b or any other style on its own doesn't mean that the content is easier to understand.

I knew basically 0 about AD and didn't know where to start since all the articles, websites ,books etc that I have looked into, explained everything in a really comlicated way. I would like to thank you immensly for this very informative yet simple video! Now I know enough to dive deeper into the concept. This video was all I needed. Keep up the great work! You got yourself a new follower.

Man this is pure gold. We all use this stuff but hardly have a clear idea about it's nitty-gritties. Thanks for thre awesome content and presentation, keep it up! :)

Class act, being concise and clear at the same time is no easy feat. Thank you.

Best summary I've ever seen !

This is so well explained! I love the quality of your videos!

Your way of explanation is outstanding.....love from india sir♥️

Well done, superbly explained in context of other differentiation methods. Exactly what I needed!

Went from complete ignorance to understanding in 15 min. Thank you!

This is highly motivated by Andrej Karpathy's lecture, but very clear explanation. It is indeed a good addition to my resource list.

Excellent video Ari. Thanks for such a great explanation! Also, your animations were really well done. I suspected you might be using manim based on the style and then I read the description :)

Very good job. One of the very few good videos I've seen around about autodiff.

This is a neat summary that's hard to find in a single place!

very well explained video with lovely calm background music. i need to brush up on my vector calculus and come back but this gave me a good intuition. hope you make more of these!

Fantastic video on autodiff, really cleared up a lot of things I wasn't sure about.

Amazing explanation of Autograd and wonderful visualizations!!! Thank you so much.

Thank you Ari. I used symbolic computation in the past but this novel way of calculating derivatives is quite interesting. Learnt lots by watching your video. For sure, I will follow up with the recommended literature

Excellent presentation mate. That was an awesome explanation and a nice trip down memory lane (university days).

Thanks for the great summary and the nice video.

Great video, well presented, clearly explained, nice visualisation... Thank you!

Thanks a lot. This kind of videos are really a lot of hardwork to produce. Thanks a lot

Thanks you so much. This video really helps me to understand a little more what is automatic differentiation is.

In order to explain this to my wife, I differentiated voter rights-the analog process humans decide who should be allowed to vote, someone who looks like me, or everyone?. I think she got it. Brilliant Ari

more videos please, this is amazing!

Brilliant video, Ari. Thank you!

Exactly what I was looking for! Thank you :)

Looking forward to your future videos

Thanks! I never understood this before, but it became obvious in one second.

Awesome presentation! I understand autodiff a little bit more. I'll rewatch several more times in the future to understand it better till I completely understand it :)

Amazing video! Very well explained.

Awesome Explanation..!!!!! Keep rocking..!!!

I like your tutorial video because it is short and good

Just as a note for anyone wondering, the arxiv link doesn't work because it includes the closing parenthesis. Otherwise great video!

Great video!, I love your content, hope you will keep making many more :)

Very intuitive explanation, thanks

Bravo! I have a final today and now I get it!

This is exceptionally well explained.

@KulvinderSingh-pm7cr

Жыл бұрын

And thanks a lot for references too, they're very useful.

Ari, this is great content! I used to call symbolic differentiation 'analytical'. It is obnoxious to track all of the coefficients.

Beautiful and clear!

Great lesson! Thank you.

Awesome summary👌

So is so great content, please keep making more :)

Thanks! This is phenomenal!

Wow, that's great explanation.

This video is genius! love it.

Excellent video!! thank you so much. I have a question: is there any AD reverse mode based on dual numbers?

timely information about source code manipulation and google tangent. It was a kind of confirmation for me that it was indeed possible. I started to learn meta programming hoping to generate code for the differentials, based on the function, without actually knowing if it was possible., basically a shot in the dark. cheers

Great video!

Great video! Where can I learn more about the rounding and truncation errors plot at 2:06? I need to make an analysis of these errors for a project. Thanks :)

great great great , Thanks a million 😃

Thanks for putting out such a great video. Im still a bit confused why forward mode AD requires a separate forward pass for each input variable. In Bayden et al. it says "Conversely, in the other extreme of f : R^n → R, forward mode AD requires n evaluations to compute the gradient". But I dont see why you couldnt compute the primal table and then the tangent table for each n variables, unless "n evaluations" means n evaluations of the tangent table and not forward passes.

That was awesome, thank you

Beautiful video but I lost track quite a few times, is there any pre-requisite topics/stuff I should know before trying to understand this

thanks for sharing

This is quite good. I’m wondering if a part 2 digging deeper yet into how the implementation takes advantage of the concept you introduce here would be possible?

@ariseffai

3 жыл бұрын

Thanks Bryan. That's a possibility. It would certainly be interesting to dig deeper into the implementation schemes, which were only briefly described here. In the meantime, check out some of the links for further information on implementations.

Love it!

@Ari, this is really great! 🤩🤩🤩

@ariseffai

2 ай бұрын

Thanks Alfredo!

Great video, thank you! Just a question, you said a main problem with symbolic differentiation is that no control flow operations can be part of the function. Is that in any way different for Automatic differentiation?

Thank you so much!

Loved this video! Are you using 3b1b's Manim?

@ariseffai

3 жыл бұрын

Yep! Manim is awesome

Wonderful video. The detailed example helps tremendously. And I think there's an error: At t=6.24, sInce $v_6 = v_5\times v_4$, in $\dot{v}_6$ shouldn't there be a plus sign where you've got a minus sign?

Hello! Can we use dual numbers for integration?

12:26 May I ask where you got that c

@ariseffai

3 жыл бұрын

Baydin (arxiv.org/abs/1502.05767) references this bound in Sec. 3.2. I don't have the exact location for it in Griewank and Walther.

@UnnamedThe

3 жыл бұрын

@@ariseffai Thank you a lot! That is already very helpful.

thanks for this!

slight type : @6.29 : v6' = v5'v4 + v4'v5. ( there should a + instead of - )

Thanks!

어쨌든 요점은, 모든 것을 다 closed form으로 저장해서 gradient를 매번 구하는 게 아니라는 점이다. 한 번 계산할 때마다, output value와 더불어 gradient value도 함께 계산해두어, 나중에 forward / backward 할 때 사용한다.

Awesome explanation, thanks! But I still have one question, can someone explain please, at 12:05, right column (Adjoints) I don't understand how did we get these values (f. e. v bar 5 = v4 * v bar 6, etc...) From where did these values come from? If we use the formula at the previous slide with sum of children nodes, I get different values..

@MarkKrebs

2 жыл бұрын

Hi I have same Q. The moment when adjoints are defined is a break to me. vbar5 = v4 * vbar6 seems "backwards." I see it matches the formula given on the prior graph page, but not the intuition for it. "The sum of the output values, weighted by my leverage in creating them," is as close as I can get.

@abhaysolanki9284

2 жыл бұрын

I know when he said children I automatically thought of v3 and v4. But instead the children in the case v5 is only v6. And children for v4 are v5 and v6. Children are the nodes that the node is pointing to.

Thanks for the superb video. I think you made a little mistake in the forward mode example at 6:24. Shouldn't it be v̇_6 = v̇_5*v_4 + v̇_4*v5 ?

@ariseffai

3 жыл бұрын

Thanks Paul, good catch-placed this under errata.

Very useful

One more note ARI. I think there is a small typo. From minute 7:36 until 7:46 the derivative of V6 should be a "+" instead of a "-".

this is very good. but some of the stuff moves too fast and not explaining things like the primal part clearly enough

Please reply to my Question. Where do you learn these and how are you able to grasp them completely, I'm a data science student and i need to know it badly. Pls share insights.

@ariseffai

3 жыл бұрын

I found the survey by Baydin et al. to be particularly helpful. See the description for links!

Great summary, Ari. Thank you. I think there is small error in 6:23. v6' = v5'v4 + v4'v5 and not "-".

@ariseffai

3 жыл бұрын

Thanks Lior, good catch-placed this under errata.

thanks bro!

Derivative at 0:43 would actually be: f' (x) = (2x)e^(2x-1)- 3x^2 ... would it not? Great video.. I've subscribed! I'm just learning derivative and chain rule so I want to be sure I'm understanding the concept/rules/procedures correctly. I'm probably wrong though, that's why I'm asking for verification... thanks!

Reverse-on-Forward sounds like ACA.

THANK YOU LORD THANK YOU JESUS AND THANK YOU SIR

not mention *dual number*?

Wooow

4:27 automatic differentiation ...

Fking Great!!!

chain rule rules

Reddit gang?

Do you get paid to make such videos? Definitely should

You should point on the screen what you are talking about when doing examples.

please no music, fantastic otherwise

on expression-swell: one of my proudest computations (and hard to debug code) is the automated differentiation 3rd derivative of the general quotient rule within [shadertoy ... /WdGfRw ReTrAdUi39] , with identical parts already pre-multiplied out by how much it is constantly repeated. webgl code: Struct d000{float a;float b;float c;float d;};//1 domains t,dt,dt²,dt³ , sure, this could just be a vec4, but i REALLY needed my custom labels for debugging. d000 di(d000 a,d000 b){return d000( //autodiff up to 3 derivatives for division , up to 3 iterations of; quotient rule within chain rule) a.a/b.a //0th derivative, simple division ,(a.b*b.a-a.a*b.b)/(b.a*b.a) //dx first derivative ,((a.c*b.a+a.b*b.b-a.b*b.b-a.a*b.c)*(b.a*b.a)-2.*(a.b*b.a-a.a*b.b)*(b.a*b.b))/(b.a*b.a*b.a*b.a) //dxdx second derivative ,((((a.d*b.a+a.c*b.b+a.c*b.b+a.b*b.c-a.c*b.b-a.b*b.c-a.b*b.c-a.a*b.d)*(b.a*b.a) +(a.c*b.a+a.b*b.b-a.b*b.b-a.a*b.c)*(b.b*b.a*b.a*b.b)) +(-2.*(a.c*b.a+a.b*b.b-a.b*b.b-a.a*b.c)*(b.a*b.b) +(a.b*b.a-a.a*b.b)*(b.b*b.b+b.a*b.c)))*(b.a*b.a*b.a*b.a) -((a.c*b.a+a.b*b.b-a.b*b.b-a.a*b.c)*(b.a*b.a) -2.*(a.b*b.a-a.a*b.b)*(b.a*b.b)) *4.*(b.b*b.a*b.a*b.a))/(b.a*b.a*b.a*b.a*b.a*b.a*b.a*b.a)) //dxdxdx //3rd derivative quotient rule sure is something ;}

Thanks!!!