Hello guys.
So in this video I'm going to talk about NLP in deep learning.
Till now if I talk about machine learning with respect to NLP, we have discussed about various topics,
you know, and all these topics were involved wherein we were specifically working with text data,
right?
And with respect to this particular text data, if we really wanted to convert this into some vectors,
which is some numerical representation, right?
Numerical representation.
We used various techniques.
The first technique, uh, if you remember, in NLP, in machine learning we discussed about one hot
encoding.
Right.
Then we went up with Bag of Words.
And the third topic that we discussed was with respect to TF-IDF.
Right.
Coming to the fourth one, we discussed about word two vec.
Along with this, we also saw about average word two vec.
And we saw a lot of practical application.
Uh, we took a specific data set and we tried to solve problems like sentiment analysis, sentiment
analysis and text classification.
Right.
We did this kind of task.
Text classification in practicals.
Okay.
Now, now when we move one step ahead, that is nothing but NLP in deep learning.
Now, as you all know, this text data are nothing.
But these are sequential data.
And if I just give you a brief information about an.
We learned about artificial neural network and this artificial neural network.
Let me just go ahead and write it down again.
This artificial neural network.
Was used to solve various problem statements, specifically supervised problem statements like classification
and regression.
Right.
It was used to solve classification and regression.
Now, when we use this classification or regression here, you can consider that I had some kind of
data.
And this data was nothing but it was a tabular data.
What do I mean by tabular data.
So this was nothing, but it was a kind of tabular data.
Now when I say tabular data, let's consider an example.
Suppose I have a house prediction.
House price prediction data set okay.
In this specific data set you will be seeing that I may have some features like what is my house size,
what is my number of rooms in the house and what is the price of the house?
So when we have this kind of tabular data.
Okay, I'll be having a lot of key points over here.
I will be considering this as my output variable, my dependent variable.
And then I can probably go ahead and solve a regression problem statement for this because price is
a continuous value.
If my output feature is a categorical value, then I can probably solve it with the help of classification.
Over here, all these features that you will be seeing, right?
If I probably change the order of this particular feature, let's say.
And for this right, if I really want to solve with the help of artificial neural network, since I
have two inputs over here, I may go ahead and create this kind of Ann.
Right.
So let's say this is my input layer where I will be giving my two features.
Let's consider this is my f one.
This is my f two and this is my y right.
So here I will be giving my f one feature.
I'll give my f two feature.
And then along with this I may have some hidden layers.
Let's say this is my first hidden layer.
And as discussed all this input feature will be connected to this particular hidden layer over here,
over here and over here.
Right.
And let's say this is my output feature and this will also get connected.
Right.
And finally I get my y hat.
So in the end what we do is that we give this feature f1 and f2.
And then we initialize some weights.
Let's say this is W1W2W3W4, W5W6 weights.
And here also we have W7W8W9.
Right.
So based on this particular weights we do forward propagation okay.
And then we do backward propagation.
Now in the forward propagation what we do we basically take the input multiply the weights.
And then along with this we also add a bias.
So let's say this is b one.
This is b two and this is b three.
And then we apply an activation function.
And similarly we do all the steps in the all the nodes.
And finally we get the y hat.
Then we calculate our loss.
Loss is nothing but y minus y hat.
Let's consider this as an example y of I minus y of I hat.
And then based on this particular loss, our main aim is to reduce this loss.
So in order to reduce this loss we need to change all these weights.
And that usually happens in the back propagation.
And that is how we basically solve this problem.
Now one very important point in an is that if I interchange this feature, let's say that if I change
the order of this feature, let's say that if I initially I had this f one, f two, and y.
Let's say my first feature was house price and the second feature was number of room.
Even though I make this feature as F2F1, then also we will be able to train this particular model with
the help of Ann.
Now, one very important information about tabular data is that here tabular data you have features.
You have some values like how we have our data in our CSV format and all.
And the second thing is that over here your sequence of data does not matter okay.
So this is very much important point.
Your sequence of data does not matter.
That basically means you can change the sequence right?
While training it.
You can keep F2 first, F1 first.
Let's say if you have F3 feature again, keep f2 f3 first because at a time this entire row will be
sent for training, right?
So here we are giving our F1 and F2.
So this will be for my first row.
Then for my second row right.
Based on the batch size we will be giving this entire data and doing the forward and the backward propagation.
Now this is fine right.
And this is with respect to an.
Now if I consider the second example that we have seen already in our session, that is nothing.
But it is about CNN.
CNN basically means convolution convolutional neural network.
Okay.
And here with respect to convolutional neural network you use this for.
So basically over here your data will be in the form of images.
It can be in the form of images.
It can be in the form of video frames.
Right.
And here most of the tasks that you specifically do right.
Some of the examples are like image classification.
right?
You have image classification.
Let's say you want to do object detection.
So you use some higher variants of CNN like our CNN.
You know you have YOLO.
All this kind of algorithms you have.
Right.
So this is kind of task you will be able to do with the help of CNN.
Okay.
Now this is fine.
And this is also fine over here.
Uh, now let's talk about the third thing.
And and that is where we are going to come up with.
That is nothing but an LP in deep learning.
The third thing that we will be specifically talking about is for a data that is of sequential type,
sequential type.
Or I can also say this is nothing, but this is my sequential data.
Now what exactly is sequential data?
Some of the examples that I will be considering over here.
Let's say this is one of the example that I will say, um, I have a data set which involves some kind
of text.
Okay.
And uh, or let me just take one example over here.
I'll just go ahead and write it down.
So let's take this example.
Text generation Now text generation is a kind of application where I will be having some kind of input.
Right.
Let's say if I say hey, how are or I'll say this is a apple juice right now, juice, I'm not giving
the word right.
So what my model should be able to do is that it should be able to generate this specific word.
It can say, hey, it is an apple juice or it is an apple pudding something.
Right?
So it should be generated.
It should be able to generate this particular output.
Now in this kind of scenario, the data that you specifically have is a sequential data.
The sequence is very much important to understand the context of this sentence.
Right.
So this kind of data is basically called as sequential data.
Let's take one more example.
The second example that I will be probably taking is nothing but your chat bot conversation.
Now in a chat bot conversation, what you do is that you basically have a queue and a chat bot right
now in Q and a chat bot, let's say if a user is asking any question and based on this question, the
chat chatbot should be able to give an answer.
So here, both on this question and answer, the sequence of information is very much important.
The sequence of text is very much important.
And based on the question since the chatbot is answering, the context of the question is also very
much important.
Okay.
So that is where you can consider this as a chatbot.
Conversation is another one example where our data set will be a sequential data itself, right?
Because see what what is a sequential data?
I'll just give you a basic definition.
Okay.
Sequential data.
A sequential data is something like this.
If I take one example I write, I go and say, hey, the food is good, right?
Now when I give this and when I see this data right here, the sequence of this data is very much important
because based on the sequence, the the sentence is meaning making some meaningful meaning over here.
Right.
If I change the order, if I just go ahead and say, hey, good, the food is if I go ahead and write
this kind of conversation and if I probably see the sentence over here, right over here, the see sea.
Based on the sequence of these words, the meaning may change, right?
The meaning may change.
Right.
So this is just one example of sequential data, where ordering of this word is very much important
because it makes sense of the entire sentence itself.
Okay.
So if I probably consider one more example with respect to the sequential data, one more application,
uh, let's go ahead and see.
This is nothing, but this is called as language translation.
Language translation.
Now see, in language translation, what do we exactly do?
Let's say I have a text in English.
I want to convert this into French.
Okay.
I want to convert this into French.
And I hope everybody has seen Google translation.
As soon as I keep on writing the word in English, automatically, you'll be able to see that it'll
be able to generate the sentence in French.
So over here also sequence of information is very much necessary.
Okay, one more example.
I'll be giving you tons of examples.
Now this is nothing but auto suggestion.
I hope everybody has been using LinkedIn has been using Gmail right over there Whenever you are writing
a mail, whenever you are writing a message, you will be able to get auto suggestion with respect to
the sentences with respect to the mistakes that you do.
Okay, so this is also one very good example, the fifth kind of example that I'm actually going to
take.
Let's consider my sales data.
Now what is so amazing about this sales data.
Your sales data is based on date time, right?
So if I go ahead and plot this entire data, let's say this is my date and this is my sales, right?
So I may have this kind of graph.
Right.
It may be increasing or it may be decreasing.
Right.
So this is basically my sales data.
Right.
And over here also this is a sequence of information.
Right.
Why I'm saying you.
Because one of the use cases that you'll be seeing is something called a sales forecasting.
And probably we can solve this particular use case by just taking the previous timestamp.
And we need to predict the future future date what will be the sales.
Right.
So that kind of forecasting can also be done okay.
So in short I've given you so many different examples to just make you understand about sequential data.
Now, in order to work with a neural network and to or in order to work with sorry, in order to work
with this kind of sequential data and probably use a neural network we cannot directly use.
Okay, here I'll write.
Can we use an to solve this problem?
Okay.
Can we use an to solve this problem?
Right.
This is what I'm going to discuss in my next video.
But I hope in this particular video you have understood, right?
Since we are discussing about an LP in machine learning.
Okay, sorry, an LP in deep learning, I had to give you a brief idea about what does exactly n n do
c n n do?
And then we discussed about sequential data.
Now my next question is that can we use N to solve this problem which has sequential data which has
sequential data.
And this is what I am going to probably discuss about in my next video.
Uh, then, uh, you know, while we are discussing this, I'll also give you a brief idea.
Like what?
All things we really need to learn in NLP, in deep learning.
Okay, so just to give you an idea what all things to learn over here.
So NLP in deep learning involves lot of amazing topic topics right.
Lot of amazing topics.
And this will turn lead to right now generative AI is in trend, right?
Generative AI is in trend.
Llms are in trend.
Right.
Multi models are in trend, right?
If you really want to learn this right, this is the prerequisite.
You really need to know each and every architecture with respect to all the neural networks that we
specifically use for NLP techniques in deep learning.
Okay.
So first uh topic that we will be learning in NLP in deep learning is nothing, but it will be about
simple RNN.
Right?
After learning simple RNN we will be seeing what is LSTM and GRU RNN.
Okay, so this will be my next RNN that we will be discussing.
Then after this you will be seeing that we will also be learning about bidirectional RNN.
Bidirectional RNN.
Then once we complete this, the next step will be that we will go ahead one step more and then we will
be discussing about something called as encoders and decoders.
Right.
Encoder and decoder.
And finally after doing this uh we'll be seeing about what is self-attention.
And then we'll also be seeing transformers right now after we see all these things.
Right now, most of the variants of LLM models that you will currently see in the generative AI field,
specifically of different types of transformers, right.
And that is where, uh, you will get a brief idea about how to work in generative AI, right?
Specifically with the help of LLM models like, you know, about OpenAI LLM models.
Right.
So we'll be discussing this step by step.
We'll be understanding the architecture.
We'll be solving some problem statements and we'll be learning this okay.
So this was a brief idea uh, about uh NLP in deep learning.
And trust me guys, these are like a prerequisite to probably enter into the generative AI field and
work with LLM models.
Even in interviews, they still focus on all these kind of questions.
So we will be solving step by step.
Each and every thing will be discussing, will be understanding the theoretical intuition and will also
be understanding the practical implementation.
So yes, now in our next video we will try to answer this question.
Can we use Ann to solve this problem statement with sequential data.
Right.
So yes this was it for my side.
I will see you all in the next video.
Thank you.