Hello guys.
So we are going to continue the discussion with respect to NLP with deep learning.
In this video and in the upcoming series of video, we are going to discuss about Transformers.
Now Transformers is a very important topic.
Uh, please keep a note of that.
You know, if you really want to excel in deep learning, specifically with respect to NLP task, um,
then Transformers is the thing.
Uh, in this video, uh, I'll just show you a plan of action.
Like what?
All things.
And how we are going to cover this entire topic.
Um, till now, we have already discussed about our an LSTM, gru RNN.
We understood what were the problems over here.
Right?
Then we went to encoder decoder architecture, which was in sequence to sequence learning.
Then uh, over here also we face some kind of problems and then we try to solve that particular problem
through this attention mechanism.
Right.
Um, again, on all this architecture, there were some or the other differences.
Right.
And now finally, we will first of all understand what is the problem in attention mechanism and what
kind of problem we are solving it with the help of transformers.
Um, the plan of action will be that, first of all, uh, we will go ahead and understand why.
Transformers.
Then we will see the architecture of the transformer.
So this is the detailed architecture which is basically shown on the right hand side.
And this architecture looks really cumbersome right.
Probably if you are seeing it for the first time, there are so many things over here and it does not
even match from the architectures which we have learned in encoder, decoder or attention mechanism.
Don't worry, we will break this down.
The entire architecture.
So in this architecture also you have this encoder.
You have this decoder.
But inside the encoder and decoder there are many more things that is actually included which we will
be discussing about it.
Right.
So the plan of action will be that first of all, we will try to understand why Transformers.
Then we will go with the architecture of transformers, wherein the first model that we are going to
cover is something called as self-attention.
And this self-attention you will be seeing this key.
Uh q uh q k v parameters.
So we will also be understanding what exactly this is.
Okay.
We basically say this as query key value pairs.
Okay.
What exactly it is.
We'll discuss about it.
Then we will be talking about positional encoding.
Right.
This is also a very important topic.
Uh in this architecture.
Then we will be covering about Multi-head attention.
And finally we will be combining to all these topics to understand the working of the Transformers.
So this is the plan of action, how we are going to cover this particular topic.
But just to give you an idea why Transformers is really important, because right now, if you if you
have heard about generative AI, right, and the kind of LLM models or multi models that are available
in generative AI, right.
Most of the models, these are basically trained on by using this transformer architecture.
Right.
right?
Specifically, if I talk about some of the models, like Bert, or if I talk about GPT, right.
Uh, and if you know about GPT right now, chat GPT has uh, sorry, OpenAI has come up with this amazing
models, right?
OpenAI specifically or chat GPT applications.
And right now the model that it is using is nothing but GPT four.
Oh, right.
Four oh.
So this specific model, you know, it is based on the transformer architecture, but it is trained
with huge amount of data.
And obviously if I talk about this GPT, it is like GPT four is, uh, using this GPT architecture along
with the transfer learning with respect to this particular architecture, which is trained with huge
amount of data.
So we will be covering all these topics.
But, uh, in this video, I just wanted to give you the about the plan of action, how we are going
to cover each and every specific topics.
So yeah, this was it.
Uh, in my next video I will be talking about why Transformers?
And again, uh, before that, we really need to again revise this encoder decoder architecture and
attention mechanism.
And what is the problem that we are facing over here?
We will be talking about it.
Okay.
Uh, yeah.
This was it.
I will see you all in the next video.
Thank you.