Attention is all you need (Transformer) - Model explanation (including math), Inference and Training

A complete explanation of all the layers of a Transformer Model: Multi-Head Self-Attention, Positional Encoding, including all the matrix multiplications and a complete description of the training and inference process.
Paper: Attention is all you need - arxiv.org/abs/1706.03762
Slides PDF: github.com/hkproj/transformer...
Chapters
00:00 - Intro
01:10 - RNN and their problems
08:04 - Transformer Model
09:02 - Maths background and notations
12:20 - Encoder (overview)
12:31 - Input Embeddings
15:04 - Positional Encoding
20:08 - Single Head Self-Attention
28:30 - Multi-Head Attention
35:39 - Query, Key, Value
37:55 - Layer Normalization
40:13 - Decoder (overview)
42:24 - Masked Multi-Head Attention
44:59 - Training
52:09 - Inference

[@umarjamilai]

Slides' PDF: github.com/hkproj/transformer-from-scratch-notes

[@bhaskartripathi]

I am not able to download the pdf file. My friends also tried. Will it be possible to put it on a downloadable link please? your content is too good and needs to be read again and again.

[@mahek6110]

its getting downloaded@@bhaskartripathi

[@hackie321]

The best Transformer explanation on internet till now and I have seen almost all of it. Kudos! You are a true teacher. I dare to compare you with Andrew NG. Please become a professor and not a corporate slave.

[@gabrielnsionu8583]

This is arguably the best explaination of the multi-head attention in the internet hands down. Very thorough and most important to folks like me using attention mechanism as my underpinning mechanism in developing my novel neural architecture to be applied to my deep reinforcement learning architecture. Sir, pls never stop making this type of videos.

[@umarjamilai]

You're welcome! 🤓

[@csikel22]

I couldn't agree more. Best video on transformers I have seen so far. I doesn't get clearer than this. It would be very interesting to give some insight why this whole thing works and what are other variations and alternative architectures.

[@rkbshiva]

​@@umarjamilaibro you're a legend!!!!

[@pablofe123]

There are still a couple of things that are not explained well in the video. Q, K and V matrixs are the same matrix? and where do the parameters matrix Wq, Wk and Wv comes from? Besides that, excellent video.

[@peregudovoleg]

@@pablofe123 21:25 "QKV are the same matreces". As for W matrices, he only says that they are "parameter matrices", and parameters is something we train during training process.

[@JulianHarris]

I'm so glad I found this again. Do NOT rely on KZbin watch history it doesn't look at all your history. This is definitely the best explanation of transformers and attention and believe me I've watched quite a few! Kudos again Umar.

[@umarjamilai]

You should subscribe to the channel to never lose it 😇 thanks for the kind words.

[@kerrykilian9127]

best explanation of the paper on the whole internet

[@orevjoker8332]

I hardly ever comment on youtube videos, but wow this was a very well done video!

[@DembaDiop-om3gv]

The best explanation of "Attention is all you need" from my point of view, guys "This explanation is all you need". Thank you very much

[@Udayanverma]

I would understand much deeper with your explanation. Rest of the world is scarying with diagrams and tables without explaining practical implementation. thank you dear!

[@keviny2]

Thanks Umar for the amazing video. This is the most comprehensive yet understandable walkthrough of the transformer architecture that I came across. Super helpful. I feel like I have a good foundation for tackling more complex LLMs because of it.

[@sanskargupta7085]

I feel lucky enough to have come across this channel, amazing stuff!

[@tariqkhan1518]

TBH The best Explanation of Attention in whole Internet.

[@KunalTiwariBCI]

Bro, legit the best explanation I have ever seen so far.

[@utkarshashinde9167]

I cannot tell you how grateful I am for this explanation provided by you .............. nowhere I find this detailed and easy-to-understand description, a go-to video for every interview preparing students

[@vitoroliveiradesouza4214]

I'm really glad to have found your video! Congratulations on the clean and yet detailed explanation

[@ajithshenoy5566]

Bless you Umar One of the finest tutorials out there. Please don't ever stop. We're willing to support you in every way possible.

[@abc-by1kb]

Such a great video! Explained all the key concepts so clearly and precisely while giving very nice intuition!

[@shashankreddyboyapally4069]

The queries keys and values are not divided by just seperating them but they are made into the size of the head by multiplying with a weight matrix which is learnable parameter, it is also applicable to the self attention algorithm

[@sushantpenshanwar8038]

You did the best job of describing the complicated details in a fluid manner. Sat, watched and took notes in one sitting. Hands down best one so far.

[@silasnginyo7744]

So far the best laid out presentation of Transformers I have ever walked through

[@Patrick-wn6uj]

This is the most important channel I have come across on youtube. keep creating these long form videos you are saving our lives in a huge away

[@brunogatti383]

Best video for attention mechanism hands down

[@vrvlbl]

Amazing explanation. I struggled too long to understand the architecture until I landed on your video. Way to go!!

[@abhilashbalachandran7160]

super useful. I really loved how you explain this with linear algebra. Very insightful. actually easier to understand than a lot of lectures at universities

[@peregudovoleg]

38:56 normalizing in this case through layer norm, doesn't squish our values between [0, 1], but rather transforms them to have mean=0 and std=1. I know, a bit confusing, some papers mix normalization/standartization. For [0.1] the formula is different: x = (x - x_min) / (x_max - x_min). Just asked GPT, it said - "it is a DS thing to call standardization a normalization." Great video non the less. I try to rewatch it every now and then just because it is so good and helps to visualize everything.

[@Nereus22]

This is really a great video, exactly what I was searching for! Everything that you mentionned was explained in details (others are skipping a lot).

[@SagarVibhute]

Kudos on the commendable work, and simplified explanation! I appreciate that you are also trying to explain the intuition behind each step and not just math. I'll view and re-view this a few times to understand more with successive passes. Thank you!

[@cristinaballesteros93]

I have watched a lot of videos about transformers, and this is by far the best one. I finally understand how they work. Thank you so much!

[@ddstar]

Excellent. You answered a lot of questions I had about where the weights come from and how they were updated

[@AvinashKumar-pb2op]

Best Explanation Ever Existed in the whole Universe !!

[@_seeker423]

The clearest explanation of a very important breakthrough paper that I have seen on KZbin. Thank you!

[@_seeker423]

One thing that I felt was missing is the logical explanation of what is the role of value vector (V).

[@keithchua1723]

Spent days trying to understand this and I wished I had come across this video first because now I understand everything fully. Immediately subscribed, keep it up!!

[@mculabs]

Probably the best explanation of the paper and the encoder and decoder sub layers. Kudos!!

[@ishaanjoshi6959]

The best explanation of attention based mechanism I found online , thank you so much Umar for making this video.

[@NazerkeSafina]

This is brilliant. Thank you Umar for your hard work. Please keep new videos coming. You are helping immensely. May you live long and happy and healthy

[@jdbrinton]

the clearest description I've found to-date. bravo!

[@AIVidya]

One of the best transforrmers videos encountered so far.

[@AbhinavSharma-dc3kv]

the best explanation for attention architecture. kudos to you sir!

[@megatroneata9911]

After watching this video and the stable diffusion video, I can say forsure that you are an amazing teacher. Extremely digestible content and easy to follow along.

[@albert4392]

I really appreciate your talent to present knowledge. Nice explaination, thank you so much!

[@lethnis9307]

Finally, after a lot of articles and videos i found a video a could understand. Thank you, sir. I am not strong in math but i think i understood a lot with this explanation

[@sergewilsonmendy9051]

Thank you man, this is the best transformer video I've seen. Well explained and very detailed.

[@hamzaomari7052]

This is the best explanation, it took me 4 hours, to take notes and revise stuff, and going with you word by word, with intuitions, and now I feel that I truly understand the transformer architecture and the mathematical intuition behind every detail. A thing that you cannot find in any other video. Thank you so much sir, this is very instructif and helpful.

[@70152136]

your presentation skill are simply amazing!!! best video on transformers I've seen so far

[@zeeshanmehdi3994]

can't thank you enough, this is the best explanation of transformers i could find after trying for days to understand it. Thank you ❤

[@tgyawali]

Thank you, so much for putting together such a detailed video. This helps technical people who do not have a lot of experience in research but have some background in machine learning to understand this very important and historic paper in AI.

[@NJCLM]

This video is surely among the top 3 among the 50 videos that I watched to understand this subject. We are very grateful to you, keep the energy, KZbin numbers will follow !

[@marsupilami125]

Can you tell me the other 2?🙏

[@tipu461]

I really appreciate your efforts to make it understandable for us 👍. Thanks a lot.

[@saravanannatarajan6515]

One of the best videos I have seen on this topic. Thanks a lot for making it easy for us. Great effort, hats off!

[@Kingissingh08]

Best video on transformer

[@gauravmalik3911]

Detailed explanation, did great work on explaining difficult topic by dividing in chunks, I don't think any part is missed in explanation. Best Explanation

[@haoming3430]

Your video is very helpful and easy to follow. I have to say this is the best tutorial about transformer I've seen.

[@ameyadesai6382]

The best explanation on this paper, can't wait to see the other videos on this topic.

[@dalilabdouraman3557]

Definetely the best explanation of the mutli head attention with the transformer ...just awesome

[@juwanyirenda3457]

Excellent exposition! Thank you Umar for the great work.

[@debjyotimukherjee8275]

Excellent video gave a complete description with a great explanation. Looking forward to more such amazing content!

[@channel8048]

This is very clear! Better than anything I have read up till now. Grazie!

[@profyao]

Absolutely the best explanation for multi-head attention so far!

[@anirudhjoshi1607]

This is the clearest explanation on this paper I have ever heard. Always had doubts about Multi-Head attention and now finally I can visualise this 100%. Thanks a lot Umar Jamil.

[@1tahirrauf]

Umar! You nailed it. Please make more videos. It was truly helpful. Thank you.

[@lyte69]

Thank you for your great explanation and effort, this was very informative and honestly there are no problems with the video, it's only a preference for me if there was some code alongside each part explained so it's even better understood, but I want you to know that this was a huge help thank you again. ❤

[@ankitkacchap]

Awesome explanation , our professor also doesn't explain like you did thank youTube recommendation and special thanks to u

[@JohnSmith-he5xg]

The best overview I've seen. Great job!

[@subinaypanda9936]

Your explanation just hits my mind. You explained all the points, where I was facing problems to understand. It's like you can read my mind from past huh 😜. Yes subscribed.

[@madhuvamsi7055]

You've definitely earned a lifelong subscriber bro! Great video.

[@rkjellbe]

Thank you, Umar. This was very helpful and I feel I have a much better understanding of the process now. Great work!

[@ltbd78]

You are incredible. Please continue making these type of tutorials.

[@aeigreen]

great explanation. thank you for demistify trasformer. I have come to your explantion after watching countless videos on transformer, your explanation is simply the best.

[@shuchenwu170]

This tutorial translates complex and terse structures into intuitions. A masterpiece of tutorials!

[@saima6759]

transformer model never got so clear to me! thank you Umar!

[@atrijpaul4009]

Best explanation of Attention throughout KZbin!!!!! Thank you sir for making this video and helping us..

[@ActualCode0]

I like how u used examples and drew out the matrices to show what was going on in the attention block. It rly helped me understand the concept better

[@shawkontzu642]

Super on the explaining the differences between training and inferencing, that clears my confusion also in "time step = 1"

[@umarjamilai]

That was the biggest source of confusion for myself as well. Glad it helped.

[@calewang3713]

Oh Man, you deserve a Turing Award.....

[@sujeethav9885]

This is just perfect! A wholesome video on Transformers!

[@priyanjaligoel4294]

omg! I love it. Finally so many answers to my questions. I had a very abstract version of the process in my head before but now its much clearer. Thank you so much!

[@danielvillalba4457]

Lots of new insights about transformers technology, every document and video provides more details, great video sir!

[@yuk-hoiyiu7023]

The only video that explains the difference between training and inference in the Transformer model!

[@andreicristea997]

Finally the fancy "black box" called transformer became more understandable for me. Really interested in the other content you are making. Thanks for the explanation.

[@skc909887u]

This is the best explanation for an engineer for sure .love this

[@rajkrishnamurthy8474]

Love it Umar. This is the best explanation of the paper. Thank you very much.

[@BritskNguyen]

this is the best lecture on transformer one can get, period.

[@hugopristauz538]

good job - your single stepping (with remarking) is really helpful

[@nadyaabdel5559]

Amazing explanation. First time every bit is super clear. Thank you.

[@sudzam]

What a wonderful video with clear explanation! Thanks for making this and sharing with the community.

[@Zineb-ru8bp]

I was struggling trying to understand Transformers but you make it easy for me. Thank you so much

[@user-pz5nn2kg2j]

The best video explaining the Transformer so clearly I have ever seen. Thanks very much for your efforts. I really appreciate your methods of explaining every steps with a concrete examples and explicitly give the shapes of every matrices that involve. The shapes of matrices in each step are the most confusing part for me to understand Transformer models, and you make it so clear for me. Thanks a lot Umar.

[@umarjamilai]

不客气！你们可以在领英交流

[@Stephanfreund]

Awesome explanation for those who seek to truly understand the fundamentals of the most important paper of this decade

[@richeek10]

Such a nice explanation with a soothing voice. Thanks so much!

[@nirajdesai]

Brilliant explanation of basics - thanks for putting this video together!

[@aurelagbodoyetin3321]

This is a masterclass. Thank you for your work

[@MichaelJentsch]

Hi, I wanted to express my thanks for your fantastic video. Your clarity and expertise made a complex topic incredibly accessible. Your video has been a meaningful change for me.

[@umarjamilai]

Thank you for your kind words, Michael! Have a nice day

[@bsuhaib]

This is called decoding a transformer. What I really liked was explaining each chunk. That was really helpful for this topic and surely taught me the approach to decode any problem. Jazaakallah ul Khair

[@parametaorto]

Hi there! I watched it from start to end and written down all infos in my notebook, it was soooo interesting! Thank you for the explanation!! It was very clear and helpful!

[@srikanthvoleti5942]

Superb video, the best explanation, I have been trying to understand transformers for a long time and this definitely helped me a lot

[@arnonil]

Thank you for the excellent introduction. I'm looking forward to your advanced topic videos on Transformers, especially those that include examples of using Transformers for various tasks or scenarios with only the Encoder.

[@umarjamilai]

You should watch my explanation about BERT then ;-)

[@koko-wf8vz]

Thank you so much for this video, hands on the best in depth video i have seen. I love the graphical explanations, it helps to visualize matrixes for a math noob :) much love

[@somdubey5436]

you have put such a hard work to explain it so clearly.....hats off to you :)

[@LinhLe-we7bw]

Thank you so much, Guy. In my mind, this video is very clearly explain. It help me undertand complex term in Transformer

[@brothachris]

Excellent tutorial! Please keep up the great work.

[@xue8888]

Thank you man, you are amazing. Keep it up ❤ good luck, I have fingers crossed for your success