Visual Guide to Transformer Neural Networks - (Episode 2) Multi-Head & Self-Attention
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- เผยแพร่เมื่อ 28 พ.ค. 2024
- Visual Guide to Transformer Neural Networks (Series) - Step by Step Intuitive Explanation
Episode 0 - [OPTIONAL] The Neuroscience of "Attention"
• The Neuroscience of “A...
Episode 1 - Position Embeddings
• Visual Guide to Transf...
Episode 2 - Multi-Head & Self-Attention
• Visual Guide to Transf...
Episode 3 - Decoder’s Masked Attention
• Visual Guide to Transf...
This video series explains the math, as well as the intuition behind the Transformer Neural Networks that were first introduced by the “Attention is All You Need” paper.
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References and Other Great Resources
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Attention is All You Need
arxiv.org/abs/1706.03762
Jay Alammar - The Illustrated Transformer
jalammar.github.io/illustrated...
The A.I Hacker - Illustrated Guide to Transformers Neural Networks: A step by step explanation
jalammar.github.io/illustrated...
Amirhoussein Kazemnejad Blog Post - Transformer Architecture: The Positional Encoding
kazemnejad.com/blog/transform...
Yannic Kilcher TH-cam Video - Attention is All You Need
www.youtube.com/watch?v=iDulh...
*CORRECTIONS*
A big shoutout to the following awesome viewers for these 2 corrections:
1. @Henry Wang and @Holger Urbanek - At (10:28), "dk" is actually the hidden dimension of the Key matrix and not the sequence length. In the original paper (Attention is all you need), it is taken to be 512.
2. @JU PING NG
- The result of concatenation at (14:58) is supposed to be 7 x 9 instead of 21 x 3 (that is to so that the concatenation of z matrices happens horizontally and not vertically). With this we can apply a nn.Linear(9, 5) to get the final 7 x 5 shape.
Here are the timestamps associated with the concepts covered in this video:
0:00 - Recaps of Part 0 and 1
0:56 - Difference between Simple and Self-Attention
3:11 - Multi-Head Attention Layer - Query, Key and Value matrices
11:44 - Intuition for Multi-Head Attention Layer with Examples
Where's the first video?
@@amortalbeing Episode 0 can be found here - th-cam.com/video/48gBPL7aHJY/w-d-xo.html
@@HeduAI thanks a lot really appreciate it:)
Awesome...So dk value is 3?
@@omkiranmalepati1645 d_k = embedding dimensions // number of heads
Need to say this out loud, I saw Yannic Kilcher's video, read tonnes of materials on internet, went through atleast 7 playlists, and this is the first time I really understood the inner mechanism of Q, K and V vectors in transformers. You did a great job here
This made my day :,)
True
Very intuitive explanation!
Totally agree with this comment
Yes, no other video actually explains what the actual input for these are
All 3 parts have been the best presentation I've ever seen of Transformers. Your step-by-step visualizations have filled in so many gaps left by other videos and blog posts. Thank you very much for creating this series.
This comment made my day :,) Thanks!
Me, too!
Definitely agree. These videos really crystallize a lot of knowledge, thanks for making this series!
ش
@@HeduAI absolutely awesome . You are the best.
Damn. This is exactly what a developer coming from other backgrounds need.
Simple analogies for a rapid understanding.
Thanks a ton.
Keep uploadinggggggggggg plss
Agreed, very well done. You do a very good job of explaining difficult concepts to a non-industry developer (fyi I'm an accountant) without assuming a lot of prior knowledge. I look forward to your next video on masked decoders!!!
@@Xeneon341 Oh nice! Glad you enjoyed these videos! :)
Absolutely underrated, hands down one of the best explanations I've found on the internet
this channel needs more love (the way she explains is out of the box). I can say this because I have 4 years of experience in data science, she did a lot of hard work to get so much clarity in concepts (love from India)
Thank you Rohtash! You made my day! :) धन्यवाद
This is the best explanation of transformers on TH-cam.
As someone NOT in the field reading the Attention paper, after having watched DOZENS of videos on the topic this is the FIRST explanation that laid it out in an intuitive manner without leaving anything out. I don't know your background, but you are definitely a great teacher. Thank you.
So glad to hear this :)
Were you the one who wrote transformers in the fist place, because no one explained it like you did. This is undoubtfully the best info I have seen. I hope you please keep posting more videos. Thanks a lot.
This comment made my day! :) Thank you.
This is one of the best Transformer videos on TH-cam. I hope TH-cam always recommends this Value (V), aka video, as a first Key (K), aka Video Title, when someone uses the Query (Q) as "Transformer"!! 😄
😄
The important detail that set you apart from the other videos and websites is that not only did you provide the model's architecture with numerous formulas but you also demonstrated them in vectors and matrixes, successfully walked us through each complicated and trivial concept. You really did a good job!
Best explanation ever on Transformers !!!
I've been stuck for so long trying to get the Transformer Neural Networks and this is by far the best explanation ! The examples are so fun making it easier to comprehend. Thank you so much for you effort !
Cheers!
Self-attention is a villain that has struck me for a long time. Your presentation has helped me to better understand this genius idea.
Coming back after a year, just to revise the basic concepts. It is still the best video on YT. Thanks Hedu AI
The MOST MOST MOST MOST ..........................useful and THE BEST video ever on Multi head attention........Thanks a lot for your work
So glad you liked it! :)
I went through many videos from Coursera, youtube, and some online blogs but none explained so clear about the Query, key, and values. You made my day.
Glad to hear this Shubhesh :)
Better than the best Berkeley professor! Amazing!
Finally! You delivered me from long nights of searching for good explanations about transformers! It was awesome! I can't wait to see the part 3 and beyond!
Thanks for this great feedback!
“Part 3 - Decoder’s Masked Attention” is out. Thanks for the wait. Enjoy! Cheers! :D
th-cam.com/video/gJ9kaJsE78k/w-d-xo.html
The best explanation I've ever seen of such a powerful architecture. I'm glad of having found this Joy after searching for positional encoding details while implementing a Transformer from scratch today. Valar Morghulis!
Valar Dohaeris my friend ;)
Your videos are so good at getting complex ideas across in an intuited way. You are like the 3Blue1Brown equivalent for AI. Keep it up and keep producing high-quality video content, at your own pace of course 😋
3Blue1Brown is one of my favorite channels! Therefore, you comparing these videos to that channel is one of the best compliments ever. Thank you! :)
@@HeduAI yes.. this is awesome explanation comparable to 3Blue1Brown.. make more..
3 days, 16 different videos, and your video "just made sense". You just earned a subscriber and a life-long well-wisher.
I'm currently reading a book about transformers and was scratching my head over the reason for the multi-headed attention architecture.
Thank you so much for the clearest explanation yet that finally gave me this satisfying 💡-moment
This really is an excellent explanation. I had some sense that self-attention layers acted like a table of relationships between tokens, but only now do I have more sense of how the Query, Key, and Value mechanism actually works.
Finally a video on transformers that actually makes sense. Not a single lecture video from any of the reputed universities managed to cover the topic with such brilliant clarity.
best transformer explanation on TH-cam!
So glad to hear this! :D
I won't say this is the best explanation so far, but this is the only explanation. Others are just repeating the original paper.
Being a professional in this field for ~5years can say this is by far the best explanation of attention.
Amused as to why this doesn't pop up on YT's recommendation for attention at the top. Probably, YT's attention needs some attention to fix its Q, K, Vs
You made my day :)
Wow. Just wow !! This video needs to be in the top most position when searched for content on transformers and their explanation
So glad to see this feedback! :)
This is quite literally the best attention mechanism video out there guys
best, best best explanation on transformer, you are adding so much value to the world.
really good intuition of self-attention and multi-attention
I am glad to hear that :)
@@HeduAI hi, thanks for your reply. When I read some papers, they mentioned ”attention map“, is that the same thing as ”attention filter“ mentioned in your video?
The best explanation of attention models on the earth!
I just repeat what everybody else said: these videos are the best! thank you for the effort
Hands down the best video on transformers I have seen! Thank you for taking your time to make this video.
Really love coming back to your videos and get a recap on multi layered attention and the transformers! Sometimes I need to make my own specialized attention layers for the dataset in question and sometimes i dunno it just helps to just listen to you talk about transformers and attention ! Really intuitive and helps me to break out of some weird loop of algorithm design I might have gotten myself stuck at. So thank you so so much :D
This is literally the best explanation for self-attention I have seen anywhere! Really loved the videos!
Amazing video, showing how the attention matrix is created and what values it assumes is really awesome. Thanks!
Your attention to details and information structuring are just exceptional. The Avatar and GoT references on top were hilarious and make things perfect. You literally made a story out of complex deep learning concept(s). This is just brillant.
You have such a beautiful mind (if you get the reference :D). Please consider making more videos like this, such a gift is truly precious. May the force be always with you. 🤘
I don't have words to describe how much these videos saved me, thank you!
Hands down, this series is the best one explaining the essence of transformers I have found online!!
Thanks a lot, you are awesome!!!!
Cheers! 🙌
Hands down the best series I've found on the web about transformers. Thank you
Thank you for putting so much effort in the visualization and awesome narration of these series. These are by far the best videos to explain transformers. You should do more of these videos. You certainly have a gift!
Thank you for watching! Yep! Back on it :) Would love to hear which topic/model/algorithm are you most wanting to see on this channel. Will try to cover it in the upcoming videos.
This is an absolute gem of a video.
This is by far the best video to understand Attention Networks. Awesome work !!
Spectacular explanation! This channel is sooo underrated!
thanks for these great videos! The visualizations and extra explanations on details are perfect!
Excellent series. Looking forward to Part 3!
“Part 3 - Decoder’s Masked Attention” is out. Thanks for the wait. Enjoy! Cheers! :D
th-cam.com/video/gJ9kaJsE78k/w-d-xo.html
Probably the best explanation of transformers I’ve found online. Read the paper, watched Yannic’s video, some paper reading videos and a few others, the intuition is still missing. This connects the dots, keep up the great work!
I really like the fact that you ask questions within the video. In fact those are the same questions one has and first reading about transformers. Keep up the awesome work!
Awesome analogy and explanation !
Outstanding explanation and well delivered, both verbally and with the graphics. I look forward to the next in this series
“Part 3 - Decoder’s Masked Attention” is out. Thanks for the wait. Enjoy! Cheers! :D
th-cam.com/video/gJ9kaJsE78k/w-d-xo.html
Literally the best series on transformers. Even clearer than statquest and luis serrano who also make things very clear
it is impressive, you explain so complicated topics in a vivid and easy way!!!
This is the best explanation of transformers architecture with a lot of basic analogy ! Thanks a lot!
Blown away by your explanation . You are a great teacher.
That is what I'm looking for, for 3 days now! Thanks a lot!
Educational + Entertaining. Nice examples and figures. Loved it!
I am just speechless, this is unbelievable! Bravo!
Spot on analysis. Many thanks for the clear explanation.
Speaking my kind of language, all made sense when you started referencing avatar
Hahaha :D Glad to know....
Thanks for posting, by far this is the most didactic Transformer presentation I've ever seen. AMAZING!
Incredibly well explained! Thanks a lot
you are amazing. ive watched other videos and read materials but nothing compares to your videos
This is very clear and well-thought out, thanks!
This video is GOLD, it should be everywere! Thank you so much for doing such an amazing job 😍😍
brilliant explanation , your chanel deserve way more ATTENTION.
Hand down the best transformer explanation. Thank you very much!
Such a fantastic and detailed yet digestible explanation. As others have said in the comments, other explanations leave so many gaps. Thank you for this gem!
The best video I've ever seen for explaining transformer.
Thank you so much! This is by far the clearest explanation that I've ever seen on this topic
Most underrated video about transformers. Going to recommend this to everyone. Thankyou
Finally I understood the concept of query, key and value. Thank you.
That is the best explanation I've seen as a beginner in this field(I have been reading tons of articles and went through a lot of videos). Thanks a lot for your hard work. Because of my renewed understanding thanks to this video, I can now better explain this concept to others by borrowing your analogy, I hope you know that your video continues to bring compounding value to society even after 2 years of posting them. Thanks a lot Hedu!
Thank you so much! Loved reading your comment.
I agree, this is great for starting out with transformers
These videos are really incredible. Thank you!
The best video on multihead attention by far!
You are so good, thank you for breaking down a seemingly scary topic for all of us.The original paper requires lot of background to understand clearly, and not all have it. I personally felt lost. Such videos help a lot!
Bravo! After watching dozens of other explainer videos, I can finally grasp the reason for multi-headed attention. Excellent video. Please make more!
One of the best explanations on Attention in my opinion.
Hello! This is an incredible explanation of Self Attention! Thank you!
Its one of the best explainations of Transformers. Just mind blowing.
Your ability to convey concepts is quite impressive! Probably the best tutorial video I've ever seen.
From now on, every time I open TH-cam, I first check if you have a new video
It was fantastic!
I greatly appreciate it.
Thanks a lot Aria! Really means a lot :)
I'm a grad student currently applying NLP - this is literally the best explanation of self-attention I have ever seen. Thank you so much for a great vid!
If only I saw your videos earlier. As everyone in the comments says, these are THE BEST videos on the subject matter found anywhere! Thank you so very much for helping us all!
Cheers! :)
Very good explanation. Thanks!
Wow. Amazing explanation! You have a gift for explaining quite complex material succinctly.
Thanks Andrew! Cheers! :D
Brilliant presentation, it's none other than the best I've seen. Great appreciation for your work!!! Cristal clear organization.
Thanks Henry! Glad you liked it :)
You are the best😄😄, This is THE Best explanation I have ever seen on TH-cam for Transformer Model, Thank you so much for this video.
have been trying to understand this topic for a long time , glad I found this video now
You have a gift for explanations... Best I've seen anywhere online. Superb.
Ah this makes everything simple and make sense
Thanks for the easy to follow explanation !
No way. This video is insane!! The most accurate and excellent explanation of self-attention mechanism. Subscribed to your channel!
I've watched many video series about transformers, this is by far the best.
Hands down the best explanation of the use of Query, Key and Value matrices. Great video with an easy example to understand.
I can't believe how good this is.
Amazing !! loved the explanation! Subscribed
These videos are amazing, thank you so much! Best explanation so far!!
By fair the best explanation of Multi-Head Attention I've ever seen on TH-cam! Thanks!
Glad to hear this :)
This is the best. It is simple and tight. Please do more papers in the future.
Thank you so much! It's the best Transformer video ever! Really hope more on other models.
Glad to hear that! :) Do let me know if there are certain models that you would like to see covered in future videos.