The complete guide to Transformer neural Networks!

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Most underrated youtuber. You are explaining this complex topics with such an ease. Many big channels avoid explaining this topics. Really appreciate your work man.

The way you approach this topic make it so easy to understand, and I appreciate the pace of your talking. Best content on transformer.

Great overview! Thanks for taking the time to put all this together!

You are the most underrated KRplusr. This is the best video explaining Transformers completely in the most intuitive way. I started my journey with Transformers with your first Transformers video few years ago which was very helpful. Also, I am so happy to see an AI tutorial video using an Indian Language. I really appreciate your work.

Amazing explanations throughout the series, and top-notch content, as always. Waiting for a detailed explanation/visualisation of the backward pass in the encoder/decoder during training. I would appreciate it if you were thinking in the same way.

i like your visualization of the matrixes. those residual connections and positional embeddings were good details to mention here

love the visualization makes it so clear

You're explanation is the most realistic explication of the Transformer that I've ever seen in the internet.
Thanks dude.

Amazing❤ Salute to the dedication in making this video, visual explaination and knowledge.

Video quality is amazing.
Keep it up, buddy!

Awesome tutorial on application of "transformer" architecture for language translation.
This is my very first lesson on the topic and I will give a 5+ stars.
Thx dude you inspired me to subscribe to your channel -- my very first you tube subscription .
Can't thank you enough!!

Amazing video, keep up the good work. Thanks for this!!

You explain really well! I think its quite complex but as you explained it, it has become more clear. I think with the coding video, it is extremely useful

this was a brilliant video!! super comprehensive

Very well explained. Thank you.

Thank you so much for taking the time to code and explain the transformer model in such detail, I followed your series from zeros to heros. You are amazing and, if possible please do a series on how transformers can be used for time series anomaly detection and forecasting. it is extremly necessary on yotube for somone!

That was awesome. Thank you man!!!

hopefully the series is completed soon ❤️ would binge watch 😁

Thank you so much for all these videos, I have learnt a lot from your videos!!!
I thought you were from Tamil Nadu, but today I got to know that you were from Karnataka!!
Where from Karnataka? I'm staying in Bangalore, Would like to meet you in-person!!!!!

You kanada written language is really beautiful!

THIS IS AMAZING ,helped me a lot thanks :)

Great channel and very useful video, thank you very much! I will watch other videos of your channel as well.
I have a question. After you perform layer normalization obtaining an output tensor, how do you give a three-dimensional tensor as input to a feed forward layer?
Do you flatten the input?

Life saver, thank you

Very well explained 👍

Really well presented.

Eagerly waiting for the upcoming videos in the series.

Bro all of my Confusion vanished like vanishing Gradient.
Thanks. Really worth it.

Will have to brush up my basics and then come back to this.

Excellent!

hi, i really love your complete model overview!
also at 8:08 you mention that the difference between K Q V isnt very explicit to the model. what would be your personal intuitive interpretation for what a Key vector might extract/learn from a input word? i find the key conept a bit odd and wondered how the authors came up with the idea of training a Key vector(/matrix), where previous attention papers only had a value vector, which would be used in both places (K and V) of the equation .
when i think about information retrieval concepts where we have a search query and documents to be ranked, iirc the intuition there is to compute a dot product to get a similarity/relevance score between them. in my mind the concept of "how relevant is each document" isnt that far off from "how much attention should i pay to each document".
And analogously I would interpret documents to be Values, and the idea of a key seems to be absent? (unless IR in practice computes a key for each document, basically a key_of(document)-query-similarity; then i just answered the question myself).
anyways, i wondered if it wouldnt be possible to simplify the attention mechanism, while keeping it conceptually similar. not sure where i should look to get to know more about this.

My friend Ajay, your playlist "Transformers from scratch" is great. It was very appealing to me to see your block diagram representation. Waiting with great anticipation for the final video. Would you be able to make it available soon?

very well 👍

Damn, could've used a few weeks ago for my OMSCS quiz. Solid review though, nice job!

Fantastic lecture. The attention layer and their inter-relationships are very well explained. Thank you. However this and other videos gloss over the use of the fully-connected layers following the attention layer. Using FC with language model embeddings makes little sense to me. Are there 512x50 inputs to the FC, i.e., is the input sentence simply flattened as input to the FC layer?

Lovely brother. I am your Neighbour Tamizhan. Lovely brotherhood

If I can recommend a next steps to this series, going into Bert, GPT, and DETR would be lovely extensions

Thank you for all the videos about transformer. Although I understood the architecture, I still dont know what to set for the input of the decoder (embedded target) and mask for the TEST phase?

amaaazing

Amazing

Thanks!

Great video. At 12:09 , how will dividing all the numbers by 8 ensure the small values are not too small or large values are not too large? Wouldn't dividing by 8 cause a number to be 8 times smaller?

Please can you apply transformers which you have built on text summarisation. It is really helpful.

Would be nice a video like this explaining LLAMA model

concise

Very good. In general articles don´t show the dimensions when explaining. It helps a lot. Tks

Great! Still a bit too hard for me but i still learned stuff.
Question, would it be possible to use the same encoder accross multiple languages ? without retrainning it after the first time, i mean.

Do a video on this new model. Called RWKV-LM.

Can this be done in pure C++

what is the use of feed forward network in transformer ..please answer

11:08 I am sorry if I am wrong but the transposed K matrix, isn't it 50x30x64?

Without bci multi head attention process possible with human brain?

Masked multihead attention is for decoder right. Is that a typo in your encoder architecture.

What're in the feed forward layers? Just an input and output layer? Are there hidden layers? What are the sizes of the layers?

But what is the source for the kannada words that was feed in to the output?, how can we get those word in reality? could someone explain me if you are willing to. Thank you.

1kth like

First :)

background music create lot of disturbance and especially that pop out sound otherwise content delivery is best

So you're from the silicon valley of India. We all now it

amazing fluent in english speak like native speaker