Attention for Neural Networks, Clearly Explained!!!

[@statquest]

To learn more about Lightning: lightning.ai/ Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/

[@tosinadekunle646]

@statquest No problem Sir. Thank you.

[@koofumkim4571]

“Statquest is all you need” - I really needed this video for my NLP course but glad it’s out now. I got an A+ for the course, your precious videos helped a lot!

[@statquest]

BAM! :)

[@atharva1509]

Somehow Josh always figures out what video are we going to need!

[@yashgb]

Exactly, I was gonna say the same 😃

[@statquest]

BAM! :)

[@yesmanic]

Same here 😂

[@MelUgaddan]

The level of explainability from this video is top-notch. I always watch your video first to grasp the concept then do the implementation on my own. Thank you so much for this work !

[@statquest]

Glad it was helpful!

[@lunamita]

Can’t thank enough for this guy helped me get my master degree in AI back in 2022, now I’m working as a data scientist and still kept going back to your videos.

[@statquest]

BAM!

[@XDogEntertainment]

This channel is pure gold. I'm a machine learning and deep learning student.

[@statquest]

Thanks!

[@sameepshah3835]

The amount of effort for some of these animations, especially in these videos on Attention and Transformers in insane. Thank you!

[@statquest]

Glad you like them!

[@OsamaAlatraqchi]

This is the best explanation ever, not only in this video, but the entire course...... Thanks a lot...

[@statquest]

Glad you are enjoying the whole course.

[@nikolamarkovic9906]

for this video attention is all you need

[@statquest]

Ha!

[@dylancam812]

Dang this came out just 2 days after my neural networks final. I’m still so happy to see this video in feed. You do such great work Josh! Please keep it up for all the computer scientists and statisticians that love your videos and eagerly await each new post

[@statquest]

Thank you very much! :)

[@Neiltxu]

@@statquest it came out 3 days before my Deep Learning and NNs final. BAM!!!

[@statquest]

@@Neiltxu Awesome! I hope it helped!

[@Neiltxu]

@@statquest for sure! Your videos always help! btw, do you ship to spain? I like the hoodies of your shop

[@statquest]

@@Neiltxu I believe the hoodies ship to Spain. Thank you for supporting StatQuest! :)

[@clockent]

This is awesome mate, can't wait for the next installment! Your tutorials are indispensable!

[@statquest]

Thank you!

[@rajapandey2039]

@@statquest BAM!

[@rutvikjere6392]

I was literally trying to understand attention a couple of days ago and Mr.BAM posts a video about it. Thanks 😊

[@NoahElRhandour]

same :D abesolutely insane...

[@statquest]

BAM! :)

[@jacobverrey4075]

Josh - I've read the original papers and countless online explanations, and this stuff never makes sense to me. You are the one and only reason as to why I understand machine learning. I wouldn't be able to make any progress on my PhD if it wasn't for your videos.

[@statquest]

Thanks! I'm glad my videos are helpful! :)

[@sinamon6296]

Hi mr josh, just wanna say that there is literally no one that makes it so easy for me to understand such complicated concepts. Thank you ! once I get a job I will make sure to give you guru dakshina! (meaning, an offering from students to their teachers)

[@statquest]

Thank you very much! I'm glad my videos are helpful! :)

[@aayush1204]

1 million subscribers INCOMING!!! Also huge thanks to Josh for providing such insightful videos. These videos really make everything easy to understand, I was trying to understand Attention and BAM!! found this gem.

[@statquest]

Thank you very much!!! BAM! :)

[@aquater1120]

I was just reading the original attention paper and then BAM! You uploaded the video. Thank you for creating the best content on AI on KZbin!

[@statquest]

Thank you very much! :)

[@usser-505]

The end is a classic cliffhanger for the series. You talk about how we don't need the LSTMs and I wait for an entire summer for transformers. Good job! :)

[@statquest]

Ha! The good news is that you don't have to wait! You can binge! Here's the link to the transformers video: kzbin.info/www/bejne/sKm0qoeBbdaor7s

[@usser-505]

@@statquestYeah! I already watched when you released it. I commented on how this deep learning playlist is becoming a series! :)

[@statquest]

@@usser-505 bam!

[@Cld136]

Thanks for the wholesome contents! Looking for Statquest video on the Transformer.

[@statquest]

Wow!!! Thank you so much for supporting StatQuest!!! I'm hoping the StatQuest on Transformers will be out by the end of the month.

[@Cld136]

❤

[@Travel-Invest-Repeat]

Great work, Josh! Listening to my deep learning lectures and reading papers become way easier after watching your videoes, because you explain the big picture and the context so well!! Eagerly waiting for the transformers video!

[@statquest]

Coming soon! :)

[@benmelis4117]

I just wanna let you know that this series is absolutely amazing. So far, as you can see, I've made it to the 89th video, guess that's something. Now it's getting serious tho. Again, love what you're doing here man!!! Thanks!!

[@statquest]

Thank you so much!

[@benmelis4117]

@@statquest Personally, since I'm a medical student, I really can't explain how valuable it is to me that you used so many medical examples in the video's. The moment you said in one of the first video's that you are a geneticist I was sold to this series, it's one of my favorite subjects at uni, crazy interesting!

[@statquest]

@@benmelis4117 BAM! :)

[@ArpitAnand-yd7tr]

The best explanation of Attention that I have come across so far ... Thanks a bunch❤

[@statquest]

Thank you very much! :)

[@brunocotrim2415]

Hello Statquest, I would like to say Thank You for the amazing job, this content helped me understand a lot how Attention works, specially because visual things help me understand better, and the way you join the visual explanation with the verbal one while keeping it interesting is on another level, Amazing work

[@statquest]

Thank you!

[@Murattheoz]

I feel like I am watching a cartoon as a kid. :)

[@statquest]

bam!

[@Namenlos-r8f]

bu mecrada ilk defa türk görüyorum, bilg müh ögrencisi misin?

[@machinelearninggoddess]

3:14 That and the vanishing gradient problem is a key factor. NNs update themselves with gradient descent, basically derivatives, and the deeper the LSTM, the more we are applying the derivative of a derivative of a derivative so on so forth of a gradient value, and since the original loss value gradient is reduced astronomically every time a derivative, beyond a dozen or so LSTM cells the gradient might become 0 and this results in the earlier LSTMs literally not learning. So not only do LSTMs not remember stuff from previous words long away, they can't learn stuff on how to deal with previous words long away either, a double whammy :(

[@statquest]

bam! :)

[@machinelearninggoddess]

@@statquest It's a double bam but it is directed at our faces and our NN, not at the problem we are trying to solve, which is really bad :(

[@ncjanardhan]

The BEST explanation of Attention models!! Kudos & Thanks 😊

[@statquest]

Thank you very much!

[@won20529jun]

I was literally just thinking an Id love an explanation of attention by SQ..!!! Thanks for all your work

[@statquest]

bam!

[@sourabhverma9034]

This is called Luong attention. In its previous version, a simple neural net was used to get similarity scores instead of dot product which was trained along with rest of RNN, this older version was called bahdanau attention. Thank you for the amazing video, I had to watch it twice to make sense of it but it is amazingly done. If I can make a request/suggestion, showing mathematical equations sometimes helps making sense of things. So if you can include them in future videos, that would be great.

[@statquest]

I'll keep that in mind.

[@jarsal_firahel]

Before, I was dumb, "guitar" But now, people say I'm smart "guitar" What is changed ? "guitar" Now I watch..... StatQueeeeeest ! "guitar guitar"

[@statquest]

bam!

[@naomilago]

The music sang before the video are contagious ❤

[@statquest]

:)

[@gordongoodwin6279]

fun fact - if your vectors are scaled/mean-centered, cosine similarity is geometrically equivalent to the pearson correlation, and the dotproduct is the same as the covariance (un-scaled correlation).

[@statquest]

nice.

[@MartinGonzalez-wn4nr]

Hi Josh, I just bought your books, Its amazing the way that you explain complex things, read the papers after wach your videos is easier. NOTE: waiting for the video of transformes

[@statquest]

Glad you like them! I hope the video on Transformers is out soon.

[@mehmeterenbulut6076]

I was stunned when you start the video with a catch jingle man, cheers :D

[@statquest]

:)

[@rafaeljuniorize]

this was the most beautiful explanation that i ever had in my entire life, thank you!

[@statquest]

Wow, thank you!

[@saschahomeier3973]

You have a talent for explaining these things in a straightforward way. Love your videos. You have no video about Transformers yet, right?

[@statquest]

The transformers video is currently available to channel members and patreon supporters.

[@AntiPolarity]

can't wait for the video about Transformers!

[@statquest]

Me too!

[@KevinKansas1]

The way you explain complex subjects in a easy-to-understand format is amazing! Do you have an idea when will you release a video about transformers? Thank you Josh!

[@statquest]

I'm shooting for the end of the month.

[@JeremyHalfon]

Hi Josh@@statquest , any update on the following? Would definitely need it for my final tomorrow :))

[@statquest]

@@JeremyHalfon I'm finishing my first draft today. Hope to edit it this weekend and record next week.

[@chessplayer0106]

Ah excellent this is exactly what I was looking for!

[@statquest]

Thank you!

[@birdropping]

@@statquest Can't wait for the next episode on Transformers!

[@rrrprogram8667]

Excellent josh.... So finally MEGA Bammm is approaching..... Hope u r doing good...

[@statquest]

Yes! Thank you! I hope you are doing well too! :)

[@d_b_]

Thanks for this. The way you step through the logic is always very helpful

[@statquest]

Thanks!

[@familywu3869]

Thank you for the excellent teaching, Josh. Looking forward to the Transformer tutorial. :)

[@statquest]

Coming soon!

[@abdullahhashmi654]

Been wanting this video for so long, gonna watch it soon!

[@statquest]

bam!

[@souravdey1227]

Had been waiting for this for months.

[@statquest]

The wait is over! :)

[@rikki146]

When I see new vid from Josh, I know today is a good day! BAM!

[@statquest]

BAM! :)

[@frogloki882]

Another BAM!

[@statquest]

Thanks!

[@abdullahbinkhaledshovo4969]

I have been waiting for this for a long time

[@statquest]

Transformers comes out on monday...

[@weiyingwang2533]

You are amazing! The best explanation I've ever found on KZbin.

[@statquest]

Wow, thanks!

[@x7A9cF2k]

Josh! Again to geg some attention with a cup of coffee, Double BAM!!

[@statquest]

Thanks!

[@thanhtrungnguyen8387]

can't wait for the next StatQuest

[@statquest]

:)

[@thanhtrungnguyen8387]

@@statquest I'm currently trying to fine-tune Roberta so I'm really excited about the following video, hope the following videos will also talk about BERT and fine-tune BERT

[@statquest]

@@thanhtrungnguyen8387 I'll keep that in mind.

[@ArpitAnand-yd7tr]

Really looking forward to your explanation of Transformers!!!

[@statquest]

Thanks!

[@okay730]

I'm excited for the video about transformers. Thank you Josh, your videos are extremely helpful

[@statquest]

Coming soon!

[@rathinarajajeyaraj1502]

Much awaited one .... Awesome as always ..

[@statquest]

Thank you!

[@automatescellulaires8543]

wow, i didn't think i would see this kind of stuff on this channel.

[@statquest]

:)

[@theelysium1597]

Since you asked for video suggestions in another video: A video about the EM and Mean Shift algorithm would be great!

[@statquest]

I'll keep that in mind.

[@CatatanSiRebiaz]

Currently learning about artificial neural networks😁

[@statquest]

bam! :)

[@madjohnshaft]

I am currently taking the AI cert program from MIT - I thank you for your channel

[@statquest]

Thanks and good luck!

[@hasansayeed3309]

Amazing video Josh! Waiting for the transformer video. Hopefully it'll come out soon. Thanks for everything!

[@statquest]

Thanks! I'm working on it! :)

[@envynoir]

Godsent! Just what I needed! Thanks Josh.

[@statquest]

bam!

[@mrstriker1847]

Please add to the neural network playlist! Or don't it's your video, I just want to be able to find it when I'm looking for it to study for class.

[@statquest]

I'll add it to the playlist, but the best place to find my stuff is here: statquest.org/video-index/

[@akashat1836]

Hey Josh! Firstly, Thank you so much for this amazing content!! I can always count on your videos for a better explanation! I have one quick clarification to make. Before the fully dense layer. The first two numbers we get are from the [scaled(input1-cell1) + scaled(input2-cell1) ] and [scaled(input1-cell2) + scaled(input2-cell2) ] right? And the other two numbers are from the outputs of the decoder, right?

[@statquest]

Yes.

[@akashat1836]

@@statquest Thank you for the clarification!

[@yizhou6877]

I am always amazed by your tutorials! Thanks. And when we can expect the transformer tutorial to be uploaded?

[@statquest]

Tonight!

[@juliank7408]

Phew! Lots of things in this model, my brain feels a bit overloaded, haha But thanks! Might have to rewatch this

[@statquest]

You can do it!

[@Xayuap]

weeeeee, video for tonite, tanks a lot

[@statquest]

:)

[@ThinAirElon]

quadruple BAM !

[@statquest]

Thanks!

[@The-Martian73]

Great, that's really what I was looking for, thanks mr Starmer for the explanation ❤

[@statquest]

bam! :)

[@rajatjain7894]

Was eagerly waiting for this video

[@statquest]

Bam! :)

[@sabaaslam781]

Hi Josh! No doubt, you teach in the best way. I have a request, I have been enrolled in PhD and going to start my work on Graphs, Can you please make a video about Graph Neural Networks and its variants, Thanks.

[@statquest]

I'll keep that in mind.

[@Ghost-ip3bx]

Hi StatQuest, I've been a long time fan, your videos have helped me TREMENDOUSLY. For this video I felt however if we could get a larger picture of how attention works first ( how different words can have different weights ( attending to them differently )) and then going through a run with actual values, it'd be great! :) I also felt that the arrows and diagrams got a bit confusing in this one. Again, this is only constructive criticism and maybe it works for others and just not for me ( this video I mean ). Nonetheless, thank you so much for all the time and effort you put into making your videos. You're helping millions of people out there clear their degrees and achieve life goals

[@statquest]

Thanks for the feedback! I'm always trying to improve how I make videos. Anyway, I work through the concepts more in my videos on transformers: kzbin.info/www/bejne/sKm0qoeBbdaor7s and if the diagrams are hard to follow, I also show how it works using matrix math: kzbin.info/www/bejne/gaHLnoKAo7F0mqs

[@lequanghai2k4]

I am stilling learning this so hope next video come out soon

[@statquest]

I'm working on it as fast as I can.

[@RafaelRabinovich]

To really create a translator model, we would have to work a lot through values of linguistics since there are differences in word order, verb conjugation, idioms, etc. Going from one language to another is a big structural challenge for coders.

[@statquest]

That's the way they used to do it - by using linguistics. But very few people do it that way anymore. Now pretty much all translation is done with transformers (which are just encoder-decoder networks with attention, but not the LSTMs). Improvements in translation quality are gained simply by adding more layers of attention and using larger training datasets. For more details, see: en.wikipedia.org/wiki/Natural_language_processing

[@방향-o7z]

목표: encoder의 마지막 토큰인 EOS와의 similarity를 계산해서 decoder의 첫 번째 토큰을 만들자. 11:52 한 토큰에 대해: 다른 토큰 포함해서 모든 토큰 하나씩 층을 만들어서 / 각 토큰층마다 내적곱으로 EOS와의 비슷한정도를 계산. => 각 토큰마다 점수로 나옴. 12:31 그 점수를 softmax로 계산하면 0부터 1까지의 값이 나옴. 더 비슷한 것을 decoder의 첫 번째 토큰 만드는 데 이용하는 것. 13:48 decoder에서 softmax로 다시 계산해서 deocer의 첫 번째 토큰 생성. 중요한 것은 11:52에서 '한 토큰에 대해'서 계산했다는 것. - 원래는 모든 토큰 전체 층을 decoder에 보내서 decoder의 첫 번째 토큰 만드는 데 이용했다면, - attetion은 한 토큰마다 전체 층 내적곱 구해서 decoder의 첫 번째 토큰 만드는 데 이용하는 것.

[@statquest]

bam

[@yoshidasan4780]

first of all thanks a lot Josh! you made it way too understandable for us and i would be forever grateful to you for this !! Have a nice time! and can you please upload videos on Bidirectional LSTM and BERT?

[@statquest]

I'll keep those topics in mind.

[@tupaiadhikari]

Thanks Professor Josh for such a great tutorial ! It was very informative !

[@statquest]

My pleasure!

[@seriousbusiness2293]

This is like a Kids show for machine learning lol. Right in the intersection i am Looking for.

[@statquest]

bam!

[@capyk5455]

You're amazing Josh, thank you so much for all this content

[@statquest]

Glad you enjoy it!

[@abrahammahanaim3859]

Hey Josh your explanation is easy to understand. Thanks

[@statquest]

Glad it was helpful!

[@yuanyuan524]

best tutorial in youtube

[@statquest]

Thank you!

[@owlrion]

Hey! Great video, this is really helping me with neural networks at the university, do we have a date for when the transformer video comes out?

[@statquest]

Soon....

[@sushi666]

A video on Siamese Networks would be cool, esp. Siamese BERT-Networks

[@statquest]

I'll keep that in mind.

[@aniket_mishr]

Thanks for the amazing explanation. TRIPLE BAM!!!

[@statquest]

:)

[@BboyDschafar]

Attention is all you need...

[@statquest]

bam!

[@sreerajnr689]

Your explanation is AMAZING AS ALWAYS!! I have 1 doubt. Do we do the attention calculation only on the final layer? For example, if there are 2 layers in encoder and 2 layers in decoder, we use only the outputs from 2nd layer of encoder and 2nd layer of decoder for attention estimation, right?

[@statquest]

I believe that is correct, but, to be honest, I don't think there is a hard rule.

[@JL-vg5yj]

super clutch my final is on thursday thanks a lot!

[@statquest]

Good luck!

[@andrewsiah]

Can't wait for the transformer video!

[@statquest]

I'm making great progress on it.

[@guillermosainzzarate5110]

Y ahora en español? Casi no lo creo, este canal es increible😭 muchas gracias por tus videos !!!

[@statquest]

Muchas gracias! :)

[@andresg3110]

You are on Fire! Thank you so much

[@statquest]

Thank you! :)

[@imkgb27]

Many thanks for your great video! I have a question. You said that we calculate the similarity score between 'go' and EOS (11:30). But I think the vector (0.01,-0.10) is the context vector for "let's go" instead of "go" since the input includes the output for 'Let's' as well as the embedding vector for 'go'. It seems that the similarity score between 'go' and EOS is actually the similarity score between "let's go" and EOS. Please make it clear!

[@statquest]

You can talk about it either way. Yes, it is the context vector for "Let's go", but it's also the encoding, given that we have already encoded "Let's", of the word "go".

[@MelideCrippa]

Thank you very much for your explanation! You are always super clear. Will the transformer video be out soon? I have a natural language processing exam in a week and I just NEED your explanation to go through them 😂

[@statquest]

Unfortunately I still need a few weeks to work on the transformers video... :(

[@christosvoskresye]

Always be'sing and do'sing! And never cheesing or choosing!

[@statquest]

Ok! :)

[@elmehditalbi8972]

Could you do a video about Bert? Architectures like these can be very helpful on NLP and I think a lot of folks will benefit from that :)

[@statquest]

I've got a video on transformers coming out soon.

[@Fahhne]

Nice video, can't wait for the video about transformers (I imagine it will be the next one?)

[@statquest]

Yes, soon!

[@sunnywell264]

Hi @statquest / @Josh ... This is an amazing video and i had been going through your content. All of those content are some of the best explanations of AI that I have seen till date. In this video towards the end where we are setting the input values of the fully connected layer, i am not able to place the values besides the value of one of the attention value. Please confirm below if I am right: Value from Encoder Layer: let's : -0.76(1st LSTM) | 0.75(2nd LSTM) go: 0.01(1st LSTM) | -0.01(2nd LSTM) Value from Decoder Layer: EOS: 0.91(1st LSTM) | 0.38(2nd LSTM) Similarity Scores: Lets and EOS : (0.91 X -0.76) + (0.38 X 0.75) = -0.6916 + 0.285 = -0.4066 ~ -0.41 go and EOS: (0.91 X 0.01) + (0.38 X -0.01) = 0.0091 + -0.0038 = 0.0053 ~ -0.01 After Softmax Lets and EOS: 0.4 go and EOS: 0.6 Attention Value for 1st LSTM which is rolled twice(for lets and go): -0.76*0.4 + 0.01*06 = -0.298 ~ -0.3 0.75*0.4 + -0.01*0.6 = 0.3 - 0.06 = 0.24 Thus we get the following input values for the fully connected layer: 1. Value from 1st LSTM Layer(Decoder) -> EOS: 0.91 2. Attention Value for 1st LSTM Layer(Encode) wrt EOS -> -0.3 I suppose the following two values are what we get from 2nd LSTM layer which has a different initial values for initial Short term memory and Long Term memory: 3. Value from 2nd LSTM Layer(Decoder) -> EOS: 0.4 Let me know if my understanding is correct Josh.

[@statquest]

What time point, minutes and seconds, are you asking about?

[@sunnywell264]

13:52@@statquest

[@statquest]

​@@sunnywell264 The values are pretty close and probably slightly off due to rounding. Is that what you're worried about or is there something else?

[@sunnywell264]

Yes... I was worried about the delta in the values. I hope that my calculations above are correct and i am not at fault there.

[@statquest]

@@sunnywell264 It's possible that, internally, my math is not rounding at each stage, so I'd be willing to bet that your math is just fine.

[@lakshaydulani]

now thats what i was looking for

[@statquest]

bam! :)

[@shamshersingh9680]

Hi Josh, thanks again for awesomest video ever made on Attention models. The video is so wonderfully made that it made such involved concept crystal clear. However, I have one small doubt. Till time step 14:37 you explained the attention with single layer of LSTMs. But what if we have two layers in Encoder and Decoder as we have in previous Seq2Seq Encoder-Decoder video. In that case, how the attention is going to get calculated. My guess is that we will calculate similarity score between LSTM output of second layer for each token with LSTM output of Decoder and feed the final similarity score to Fully Connected Layer along with output of hidden cells of LSTMs of second layer. Or will we calculate similarity score between LSTM output of each layer in Encoder with each layer in Decoder as pass the input to the FC layer along with the output of second layer in Decoder since that is the final output from the Decoder. Thanks a lot again for being our saviour and your presence makes this the best time to learn new things.

[@statquest]

Thank you! I'm pretty sure we would calculate the similarities between each layer in the encoder with each later in the decoder to pass them to a fully connected layer.

[@aiforeveryone2941]

i hope to see cross attention BAAAAAMMM!!!!

[@statquest]

I'll keep that in mind.

[@vinisilva5647]

it would not be possible to translate the other older videos you explain very well.❤

[@statquest]

Glad you like them!

[@luvxxb]

thank you so much for making these great materials

[@statquest]

Thanks!

[@carloschau9310]

thank you sir for your brilliant work!

[@statquest]

Thank you!

[@tangt304]

Another awesome video! Josh, will you plan to talk about BERT? Thank you!

[@statquest]

I'll keep that in mind.

[@ichkaodko7020]

I just watched it bam!

[@statquest]

BAM! :)

[@arvinprince918]

hey there josh @statquest, your videos are really awsome and super helpful, thus i was wondering when will your video for transformer model come out

[@statquest]

All channel members and patreon supports have access to it right now. It will be available to everyone else in a few weeks.