Illustrated Guide to Transformers Neural Network: A step by step explanation
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- Опубліковано 4 чер 2024
- Transformers are the rage nowadays, but how do they work? This video demystifies the novel neural network architecture with step by step explanation and illustrations on how transformers work.
CORRECTIONS:
The sine and cosine functions are actually applied to the embedding dimensions and time steps!
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I must say you’ve given the best explanation on transformers that’ve saved me lots of time studying the original paper. Please produce more vids like this, I would recommend the BERT family and the GPT family as well 👏👍
I agree. I can't seem to find a good explanation on the BERT model
Sorry to be offtopic but does anyone know of a tool to log back into an instagram account??
I stupidly forgot the password. I appreciate any tips you can offer me.
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Takes a while so I will reply here later with my results.
@Matias Santino It worked and I actually got access to my account again. I'm so happy:D
Thank you so much you saved my account!
@Ronnie Adam you are welcome :)
this is great but would've loved if you could have taken a sample sentence as an input and show us how it transforms as it moves through the different parts of the transformer.
Perhaps an idea for the next video!
@The A.I. Hacker - Michael Phi please do !
The video actually led me to expect this example as well! It would be highly beneficial.
Wow , this was great, I have watched a no of videos on the transformer models, and they have all contributed to my understanding, but this puts everything together so neatly. Amazing, please keep making more such videos.
12:56 encoder has hiddens state of key-value pairs, and in the decoder, the previous output is compressed into a query. The next output is produced by mapping this query and the set of keys and values.
this is critical! I was pretty confused on this for awhile until I realized he swapped the Query and Values on accident.
ah someone else realised it. this comment should be pinned ^^
you are correct
@@mrexnx Correct me if I'm wrong but the only reason you put the mask so it doesn't attend to "future" words in the sentence is cause of the nature of the Ebglish language..since English is written left tor ought unlike other languages. Otherwise you shouldn't have thst mask because you would need to attend to words on right or maybe left also?
@@leif1075 I also thought something like that, that means, in case of Arabic, this direction of masking should not work !
There is actually a small mistake at 12:56: The Ecnoders Output are the VALUES and keys! for the Decoders second self attention.
So it is: Value and Key from Encoder with Query from Decoder are combined.
from the "Self Attention is all you need" paper: "In "encoder-decoder attention" layers, the queries come from the previous decoder layer,
and the memory keys and values come from the output of the encoder."
Ik that's true but still it doesn't make sense, What's the use of the masked attentions score that we just computed in that Multi-Head attention layer since it's not carried forward
yes you are right
I asked chatgpt (GPT4) for the same and look at the response:
"Let's break down the process:
Encoders in Transformers:
They take the input sequence and process it through multiple layers of self-attention and feed-forward networks.
The final output from the last encoder is used as the "Keys" (K) and "Values" (V) for the subsequent decoder.
Decoders in Transformers:
Decoders also have multiple layers.
In the first self-attention mechanism of the decoder, the target sequence (for example, the partially generated translation) attends to itself. This is analogous to the self-attention in the encoder but has a slight difference. In the decoder's self-attention, future tokens are masked, so they can't be attended to. This ensures that the prediction for a particular word doesn’t depend on future words in the sequence.
In the subsequent multi-head attention mechanism, the "Queries" (Q) come from the decoder's previous layer, and they attend to the "Keys" (K) and "Values" (V) from the encoder's output.
This essentially means that the decoder is using the information from the encoder to help generate the next token in the sequence.
So, your statement is correct: "Value and Key from Encoder with Query from Decoder are combined." In the Transformer's decoder, for every step in its layers, the Queries (Q) from the decoder attend to the Keys (K) and Values (V) from the encoder output."
Unless it’s a decoder only transformer
indeed it is like this as the dot product of the keys and querries construct the relation between the input and the already generated output if noy K and Q where from the encoder it wouldn't capture the relation between the input and already generated output
I used multiple sources to learn about the transformer architecture. Regarding the decoder part, you really helped me understanding what was the input and how the different operations are performed ! Thanks a lot :)
This video marks an end to my search for one place explanation of Transformers. Thanks a lot for putting this up! :)
I have been struggling with this architecture for an eternity now and this is the first time I really understood what's going on in this graphic. Thank you so much for this nice and clear explanation!
What about the architecture made you struggle if I may ask?
This is literally the best explanation of Transformers I have ever seen!
This is by far the best explanation I’ve ever seen on Transformer Networks. Very very well done
This tutorial is absolute brilliant, I have to see it again and read the illustrated guide, there are so many infos!! Thank you!!!
Strongly agree!
Man thanks for this video, reading a paper for newbie is super difficult, but such explanations like you've posted for key, value and query as well as reasoning for masking is very, very helpful. I subscribed to your channel and am looking forward for new stuff.
Thanks for your explanation, very clean and well built in every argument about transformers. I was so lucky to get this video randomly on UA-cam. Good job!
Brilliant explanation with visually intuitive animations ! I rarely comment or subscribe to anything but this time I instantly do both after watching the video. And how coincidental it is that this was uploaded on my birthday. Hope to see more videos from you.
This is THE BEST transformer video I have encountered.
Half of it went through my head. Just beautiful. I'll watch it many more times.. That's how I know the content is gooood.
That's a very good explanation imo! Thanks for taking the time to produce such a gem.
This is one of the best introductory videos I've seen on this subject. Thank you!
Best explanation I've seen so far, thanks so much! 😃
Your videos are the BEST, I understood RNNs, LSTMs, GRUs and Transformers in less than an hour.
Thankyou.
The best explanation so far. Loved the animated illustration.
This is the best video I have seen about transformers. Very articulate and concise. Great job
amazing explanation, honestly this is the first time i understand how Transformers work.
This is incredible. I've been watching videos and reading papers about transformer and attention for days, this is the best material so far.
This seems to be one of the best videos on Transformers
Incredibly interesting. It is amazing how much processing and storage is required to achieve this.
Honestly this is the best explanation I've ever seen on transformers and attention
Favourite video on the topic! I'm reasonably knowledgeable on ML, but the other 5-10 videos I've tried so far all resulted in increased confusion. This is clear.Nice one 👍🏿
I watched a second time not for better understand the video, but to appreciate it. It is very well done and pretty clear. Thank you.
This was beautiful.
This was the best explanation out there. You Sir, are a person of highest quality.
You did an amazing job explaining the workflow … looked for more similar stuff… please continue … I hope you will be back to help people like me
This illustrated explanation is just so well done :OOOOO
I'm a novice at Deep neuronal networks and just by looking at the video, I just understood everything !
Completely recommended to understand Transformers :)
Good work :D
This explanation is INCREDIBLE!!!
Such a lucid explanation it is. Thanks for posting!!
This is the best explanation ever! So genius! Need more videos like this
The explanation about Transformer architecture is clear, and the animation in presentation is really good, it catches my attention :)
Amazing explanation Michael! Thank you for your time!!!!
My first read about Micheal Phi was "Stop Installing Tensorflow using pip for performance sake!" in TowardDataScience blog (as I recall you was "Micheal Nguyen" at that time). My first impression was like "oh this guy was good at explanation". Then I read his several blogs, and now here I am. I never knew that you have a channel. You are one of the best educator I've ever known. Thanks so much.
Best video ever on transformers, trust me I tried others, just positional encoding is missing, but rest is gold.
Thank you.
Thanks, I particularly liked that you went into as much detail for the decoder as for the encoder.
Wow. This is some really good explanation! I don't have much NLP background except RNN/LSTM and things before DL (N-gram), but wanted to know more about Attention mechanism for robotics application. (my field) Most other explanation either skimmed over the mathematics, or used NLP specific nomenclature/concepts that made it hard to understand for non-NLP people.
This was some good stuff! Much appreciated and Keep up the good work!
Best video on transformers on UA-cam, thank you so much
Simple and coherent explanations. Brilliant
Great deep dive into transformers. Helped me understand this architecture.
This is the best explaination on transformers anywhere on the web
Now i have two great heroes that explain complex concept using mindblowin visualization, first is 3b1b for complex math topics, then Michael Phi for complex machine learning architecture! Just wow ... salute sir! thank you so much!
This was super helpful thank you. I read the original paper and absorbed like 70% of it but this clarified several things.
This is by far the best explanation that I have seen.
Best examplanation I have seen so far. Great job! You destroyed almost all my questions.
Happy to help!
A complex process- I need to listen to this multiple times to fully understand this.
Same here
Incredible video! I hope you are doing well and find the time to make more, especially with the recent popularity explosion of AI.
Nice work! Love the visuals for this abstract topic. Just found your channel. Keep em coming!!
Thanks! Your content is also super helpful as well and has helped me before
Thanks a lot. This is by far the most clear explanation of the paper. Kudos. Hope you can do similar videos for say Bert, XLNET architectures as well.
Very deep explanation, brilliant talent to give somebody an intuition
If you only knew how relevant this would be 2 years later. Thank you!
Wow..one of the best videos I have watched on transformers...so simple to grasp. Please make more videos.
I keep coming back to this video. It's great.
Thanks for the effort you put into making the animation on the slide.
Perfect explanation of the concept, thank you!
wow! you explained it so clearly and really helps my understanding, thanks
hands down the best explation for transformer models !
Thank you so much! Very well Explained, cleared most of the doubts.
The best video on this channel Michael. Do you think you can make a bunch more like this... with this visual style (white over black drawings), and clear and calm explanation of the diagrams.
Your explanation of q k and v is the thing that finally did it for me, I get it!
Amazing. I still don’t really understand how the Q K and V values are calculated but I learnt a lot more about this seminal paper than others provided - thank you! 🙏
Amazing! The best explanation I've ever seen.
Thank you for this amazing explanation. It really helped after an insufficient explanation from my DL lecture. The prof did not even mention that the final part is a classification over the vocabulary for an nlp task!
Wonderfully explained ! Thank you.
Dude you are insanely good! Keep up the good work!
Thank you so much for the step by step explanation. This is a good starting point for ML dummies like me to get a grasp on the transformer model architecture.
Fantastic!
Thank you for a marvelous presentation.
Thank you for this video, it's a great piece of work, so easy to understand, where others are confused in their explanation, and probably me, if I were to do it.
best ever explanation i came across, you made it very useful as well as fun to watch, thanks
Amazing! thank you so much - great quality of the video and content
Outstanding explanation and visuals. Well done.
Really nice high quality video. Much appreciated
Perfect explanation of the Transformes !!! Thanks.
Very well explained ! Keep up the good work.
This is the best explanation on transformers. Thank you so much for the video.
Wow Michael, this is a superb explanation of the transformer architecture. You even went into detail about the meaning of the Q,K,V vectors and masking concepts which were hard for me to grasp. I bounced around through 3-4 videos about the transformer arch, and for each one I claimed it was the best explanation on the topic. But your video takes the cake and explains it in half the time as the others. Thank you for sharing! Also, great job on the visuals which are on par with 3blue1brown's animations.
Thank you 😃
Very clear and clean explaining. Thanks.
Correction: The sine and cosine functions for the positional embedding are applied to the input embedding dimension, not the time steps! oof!
For the readers check out the written version of an illustrated guide to Transformers here towardsdatascience.com/illustrated-guide-to-transformers-step-by-step-explanation-f74876522bc0
thanx man . i was gonna ask have you written a papper for it like you did for LSTMs
Also the positional embedding in the illustrated guide is incorrect. Its alternating between even and odd
Your video is the best explanation of Transformers I have ever seen
How do we actually split the queries and keys into multiple values for multiheaded attention?
haha thanks micheal, I was like wtf eveything i learned is wrong at 4:18 until i scrolled down
感谢！ Thank you so much, I've looked up all the resources on the Internet but still messed up with the mechanism. It's really a clear and detailed explanation.
One word excellent , none of the explanation will match this in UA-cam ....stunned
Wow, by far the best explanation. Teaching really is an art where even experts with their blind spots can be very bad at explaining (even if they know it well themselves). Well done. It's really too bad your previous video was a year ago. I really hope you make more videos explaining other deep learning papers such as Faster RCNN etc. Thanks for posting this.
Yes it was through this video that I finally got my head around Transformers. Now I can read through more resources and the Paper itself
I watched many videos, but I'm 100% sure that this video is only the best among those in explaining the transformers
Well done, this is brilliant !
Best explanation I've seen - thanks !
Great work here!! Thank you for this excellent explanation!
Thank you! 😄
While I still have some questions, this is a pretty good explanation, I mean I actually have an idea of how this works! Gonna watch it like 2 more times.
Amazing explanation!! Please make more videos on deep learning, it would be a great help....cheers!!
Thanks a lot! Your video made things a LOT clearer for me! 🙂
Great breakdown. Really easy to follow
Best illustration on transformer.
Subscribed for many more to come.
Best Transformers Explanation I have seen thank you very much, Liked the video and Subscribed !! Keep it up :))
This was the best video i saw on attention . Thank you so much
Brooo you are seriously my god😭😭🙏🙏...thanks a lot for this video...no one... literally no one could teach me transformer and your video just got drilled into my mind...please make other videos like this for bert gpt xlnet xlm etc etc... I'm really thankful to you
Thank you so much for this explanation! You saved me a lot of time man
A really great explanation! Thank you very much!!
Good explanation. What boggles my mind is that this architecture can not only produce reasonable sentences, but there can be some logic going on behind the sequence of sentences as we've seen in chatGPT. It is mind-boggling that there must be some amount of deeper conceptualization represented in the embeddings too! amazing
No, and it's not even understandable how you got to such conclusion.