Uh, now, as you know, from the past two years, like from 2022 and until now, right?
Generative AI is really the talk of the town, you know, and, uh, with respect to generative AI,
if I talk about large language models, open source, large language models, open AI, doing some such
an amazing word.
Now cloudy three is also there to compete.
Open AI, you know, and Amazon is definitely supporting them.
So you should know right?
What exactly is generative AI?
How is it different from all these terms?
Or what is the exact difference between AI versus ML versus DL versus generative AI?
So I'll make you understand in this specific video.
Probably I'll try to make this video from somewhere between 15 to 20 minutes.
Um, I'll be including all simplistic terms, very easy definitions, so that you'll be able to understand
it, because once you have that clarity.
Right, uh, at the end of the day, right, you really want to probably become an AI engineer, you
know, which uses all these technologies to create that AI apps.
So, uh, let's go ahead and let's understand this thing.
Okay.
So as usual, when I probably start the session, you know, let us consider the entire universe.
And, uh, in this field of universe, I would definitely like to call this as AI.
Okay.
now what is AI?
Simple artificial intelligence.
The main aim is to build applications that can perform, that can perform its own task without human
intervention.
Now, this is the most important definition, right?
Without human intervention.
So it will be able to perform its entire task without human intervention.
So let me just talk about it.
Some of the examples as we know, are Netflix, right.
It has a amazing AI recommendation system.
Recommendation system which recommends movies, right?
Now here, human intervention is not required to probably provide you some kind of recommendation.
Whatever things you are using, you're browsing the movies, whatever movies you are specifically seeing,
you know, at that point of time, this AI model is actually helping you to provide you good recommendations
so that you can stay for a longer period of time.
Self driving car is another example.
Okay, so self driving car is another example.
Right?
Self driving car.
Now it is able to probably drive itself.
You know whenever a turning is coming everything it is able to do.
See at the end of the day whichever field you specifically work right.
Uh, if I talk about AI engineers, this is very much important.
If I talk about AI engineers, at the end of the day, at the end of the day, you are actually creating
an AI product, right?
And this product may be integrated with the software product itself, right?
If I say Netflix, it is a streaming platform, movie streaming platform.
And if I talk about AI engineers, you know, they are trying to integrate some amazing models, you
know, which involves fine tuning, which involves multiple things so that you integrate in such a way
that it is quite scalable.
The machine learning engineer task will also come right?
So at the end of the day, we are specifically creating an AI product, and that product is getting
seamlessly integrated with some, um, you can probably say web app or Android app or mobile app or
edge devices, something as such.
So I hope you got an idea with respect to AI.
So it is mostly about building applications that can perform its own tasks without human intervention.
Now let's talk about machine learning.
Now, whenever I talk about machine learning, machine learning is a subset of AI.
So this is basically machine learning.
It is a subset of AI.
And the main aim of machine learning is to provide you stats, tools to perform to perform various tasks
such as statistical analysis.
Statistical analysis.
Visualization.
Prediction and forecasting.
Right.
So these are some of the examples that I have just written it over here.
But at the end of the day what is machine learning.
It provides a lot of stats tools to properly perform the complete life cycle of a data science project
that is specifically required, uh, in every life cycle, from data ingestion to probably data transformation
feature engineering, you will be required some or the other concepts with respect to ML techniques,
where this is a very important term.
Specifically, if I say about that is nothing but stats tools.
Okay, so machine learning is a subset of AI, right?
And here it is providing the stats tools to perform all this task.
And this task is usually performed on what.
On data.
Now why we do this.
Why we do this.
So that so that we understand about the data.
We understand about the data.
Right.
So that a data will be meaningful, it will be able to convey some information to you.
Right.
So that is where machine learning basically comes into existence.
Now coming to the next part, that is nothing but deep learning.
So if I talk about deep learning again, deep learning is a subset of machine learning.
And if I talk about deep learning from 1950s, it's not like deep learning has become just famous right
now is it has become famous right now because of amazing things like GPUs, technological advancement,
open source libraries, and many more things.
But this, this entire deep learning was built to mimic human brain, right?
Human brain.
We wanted we wanted AI or we wanted machines to perform like how we human beings used to perform, right?
How we human being used to learn.
So that is the reason, as I said, mimicking human brain.
And that is where we have something called as multilayered neural networks.
Right.
Multi-layered neural networks.
So I hope you got a clear idea about this.
And here whenever I talk about deep learning, the three important things that we specifically learn,
right.
Please understand this because after this only will be able to understand about generative AI.
So with respect to deep learning, the three important things that we specifically learn, write and
write CNN.
And then we basically say RNN and its variants.
Right.
And this is where when I say RNN and its variants, CNN and object detection, CNN object detection
is completely for computer vision purpose.
Right.
So if I talk about task over here specifically you use computer vision over here.
You specifically use for what kind of use cases for text related use cases, write text related use
cases.
Right?
Or you can also use it for time series use cases, because that is how RNN and its variants are designed,
right?
Time series use cases.
So an is definitely like how machine learning is basically trained.
Similarly, you can train machine learning problem statement with the help of a and also.
Right.
So overall most of the things that we specifically learn in deep learning are based on these three things.
Okay.
When I say CNN object detection there are techniques like R-cnn and many more things.
And all right similarly RNN you have RNN, LSTM, RNN and GRU, then you have encoder decoder.
Attention is all you need.
Then you have Transformers and Berts right?
So I hope everybody has learned from my playlist till here.
Everything has been explained along with theoretical intuition and practical intuition.
Transformer and Bert are something very advanced and from here only we will be deriving the next thing
that is nothing but generative AI.
Okay, because this is the backbone used in most of the LLM models in generative AI transformers and
birds.
Okay, so deep learning is mostly about this specific thing.
At the end of the day, we are trying to mimic the human brain.
We are trying to understand how human beings specifically learn.
We also learn in the same way.
Now coming to the next one, that is nothing but generative AI.
Obviously, generative AI is a subset of deep learning again.
And here advanced things okay.
So I will specifically talk about two types of model that we use in data science.
Industry models are two types of model training.
We specifically say right.
One is the discriminative, uh, if I if I just want to say it is that it's mostly like discriminative
and generative models.
Okay.
So if I talk about deep learning models, they are mostly of two types okay.
This this is one and this is the other one okay.
Here you have something called as discriminative models.
Here you have something called as generative models.
Now you should understand whenever I talk about generative AI you should understand one very important
thing.
Generative AI is more about generating content okay.
So these are specific models which will help you to generate new content okay.
Based on whatever content it has already been trained on.
Okay.
If I talk about discriminative AI, it is mostly about task like classification, classification, prediction,
all this regression prediction.
So all the stocks you can basically do in short over here your data set that you have.
Right.
These are this entire discriminative models are trained on labeled data set.
You have to really understand this okay.
Labeled data set.
Similarly in the case of generative AI, if I really want to understand about the task, it is nothing,
but it generates new data trained on.
Huge data set.
Okay.
If I really want to make you understand.
Right.
In a simple way, if I just take.
Okay, I, I am a person.
What I've done is that I have probably learnt or read 100 books on cats.
Right?
So I have been trained on this many number of books, huge amount of data.
Right now what I being a person, if anybody asks me any questions with respect to cats, I will be
able to answer all the questions in my own way.
Right?
And obviously the answers will be accurate because I have already read 100 books on the cat.
So similarly, if I talk with respect to generative AI, there are two types of models that we specifically
learn.
One is large language models.
Large image models.
Large language model is with respect to text data.
Large image model is specifically with respect to images and videos.
So here if a text is given, you can convert that into an image.
If a text if a text is given you can convert that into videos here.
Large language model.
If a text is given it will be able to provide some response in terms of text.
Right.
And if I talk about all the various categories of models, just in some time I'll be explaining you
that also.
Okay, so generative AI.
In short, what it is basically doing is that here we have models that is already trained in huge amount
of data.
And the task of that specific models are based on any input.
It will generate a new data itself.
Okay.
Now to make you understand more about LM models.
Right.
So if I talk about more LM models, right.
Obviously, you know, companies like OpenAI, you know, companies like meta, Google, anthropic.
Right.
And everybody is race in, in the race, right to generate the best LM model.
So this, this, this companies are already doing really, really well.
Anthropic basically comes up with uh model which is called as cloud three.
Right.
Right now it is being a fierce competitor of OpenAI, GPT four.
Right.
GPT four.
Then you have in meta, you have open source models like llama two.
Google.
I hope everybody has heard about Gemini, right in Google.
And open source model has also come, which is called as gamma right now.
What are all these specific models?
These models are nothing, but these are specifically called as foundation models okay.
This foundation models are also called as pre-trained models.
Why?
We say it as pre-trained models?
Because these are trained in huge amount of data.
The entire internet data.
Huge data.
It has been trained in huge data.
The entire internet data.
Right.
It may be cold, it may be multiple things.
And all right, now we can use this specific model for domain specific use cases.
Also domain specific use cases also.
And that is where a concept something called as fine tuning is used.
Right.
And I have already created a playlist about fine tuning.
Also, I've explained concepts like Laura, Laura, how you can basically do the fine tuning, how you
can do it with, uh, open source models and many more things.
Right.
The fierce competition right now is basically to get the best foundation model, right?
And right now, GPT four, GPT four turbo, GPT five is also going to come right.
If I talk about open source model, llama three is there in the pipeline, right?
Cloudy has just recently launched cloud three.
Gemini is also coming up with Gemini Pro.
More variants.
It is basically coming up.
So the main race, if I probably talk about is to create the best foundation model.
Later on.
Many companies will be able to use this foundation model in the form of pre-trained models, or it will
also.
They will also be able to fine tune with some own custom data set right to solve their use cases.
So this was the entire idea about generative AI.
Again, uh, this is the video that I really needed to upload, uh, early.
But yes, many people were waiting requesting for this specific video.
So I made you understand this entire thing by writing in front of you.
So for more details with respect to generative AI Lang Chen, now, if I talk about Lang Chen, it is
a framework, right?
Lang Chen is a framework which will be able to work with all the specific models.
At the end of the day, we will be able to generate our application, our application like retrieval,
augmentation query, and along with that, we will also be able to develop some amazing chat bots.
That is the main reason, right?
We why LM becoming very famous because of the chances of getting automated right, we will be able to
automate the entire chat bot response things with respect to it.
Not only that, it has a huge scope in multiple sectors.
Also, with respect to various use cases, uh, this is mostly about LM models.
Lim models is with respect to large image models, stability.
Uh, stability I is a company which is specifically working in this specific thing.
Right.
So I hope you like this particular video for more videos related to long chain generative AI lm models,
you can follow my other playlist of long chain open AI and ah, where you'll be able to see lot of end
to end projects.
So I hope you like this particular video.
I will see you all in the next video.
Have a great day!
Thank you all.
Take care.
Bye bye.