Hello guys!
So welcome to this amazing course on complete generative AI with Lang Chen Ecosystem.
And here our main aim is to specifically focus on building amazing generative AI applications with the
help of Lang Chen.
Uh, here we are just not going to create simple AI applications here.
We are going to probably create Rag applications.
We are going to create search engines.
We are going to create text summarization application.
We are going to create agent type applications and all.
And there are lot many examples that we are going to see, uh, as we go ahead in this entire curriculum.
Uh, I have probably uploaded more than 13 plus projects, end to end projects that will be available
over here.
So end to end projects right now in this video I really want to give you the flow, like how we are
going to learn this entire course.
Okay.
So first of all we will be starting with one programming language.
This is called as Python programming language.
Okay.
Now in this Python programming language, everything that is specifically required in order for you
to get started with this particular course, everything I've actually covered in this Python programming
language, right?
And obviously when I say Python programming language, this is just not like basic type of level.
It is completely from basic to advanced, right?
So here I have actually covered amazing libraries like numpy, pandas.
I've covered inheritance.
I've covered multiple things with respect to this so that you will be able to even file operation is
also covered in this.
Right.
So you'll be able to probably implement this all this kind of projects as we go ahead.
Right then, uh, coming to the second and the, uh, important thing that we are going to learn over
here is nothing but NLP in deep learning, right?
So here we are going to probably also cover this entire NLP with deep learning, because this is something
we really need to know before we go ahead and start working with LM models and multi models.
Right.
So in NLP in deep learning we are going to learn about RNN.
We are going to learn about LSTM, RNN.
Then after covering LSTM RNN you will also be seeing we will be covering GRU RNN.
So in every RNN that we will be seeing, we will understand what is the theoretical intuition, what
is the practical intuition?
What are the problems with the previous one and why the next version of the RNN are specifically coming
up?
And if I probably consider this entire RNN, this is important because this will be useful for solving
any kind of NLP use cases.
Okay, so once we cover the RNN then we will be working with bidirectional RNN.
This is also another variant okay.
Then after getting through all these things then we will be also understanding about encoder decoder.
Right.
And this will have the entire mathematical intuition.
And this will be super important in the interviews because in interviews also they specifically ask
this kind of questions.
And right now also in interviews they focus on basic things.
Right.
So we will be covering with respect to encoder decoder.
Then along with this we will also be seeing something called as attention mechanism.
Now after seeing this attention mechanism we will be seeing some more architecture like Transformer
and Bert.
Right.
So we will also be seeing this now with respect to all these topics.
Once we probably complete it.
right, this will actually give us a basic building block to probably understand about basic building
block, to understand about what it will help us to understand about, uh, LM models.
Right.
LM models, or in short, generative AI.
Right.
So then we will probably go ahead and cover what exactly is generative AI, what exactly we'll be able
to do with this.
Right.
Uh, after understanding about generative AI, then we are also going to go ahead and see about Lang
chain ecosystem.
Right now, Lang chain is one of the most popular web framework, uh, most popular generative AI framework
because this will actually help you to develop amazing generative AI functionalities.
Okay.
API generative AI applications.
Now the best thing about plankton ecosystem?
It has almost each and every thing right?
From this you can actually go ahead and use your both paid LLM models multi model.
Not only that, in our course we are also going to cover open source LLM LM models, open source LM
models.
Uh, and this link chain ecosystem provides each and every functionality.
If you definitely want to work with agents, you want to probably go ahead and create applications with
respect to, uh, you know, uh, let's say one example that I'm going to probably take in this, you
know, we will be going and chatting with SQL database.
Right.
So this you'll be seeing here the chat will basically happen with text.
So this kind of applications will be able to generate our main aim of considering the entire long chain
ecosystem is that because it has many, many features we have vector database right.
We have vector database.
We have retrievals.
What exactly is retrievals will try to understand how to probably go ahead and create chains, how to
do the text summarization right, how to work with creating with chat bots will work with chat bots
and this chat bots will be working with chat message history, chat message history.
So there will be a lot of functionalities that we are going to break down the entire long chain ecosystem.
And in this long chain ecosystem, there are some of the very important modules that we are going to
specifically use.
Uh, if I talk about long chain, there are these three important libraries that we are going to use.
One is lang chain lang chain core.
We are going to go or we will be getting to know more about it.
What exactly is Lang chain core and why this community is specifically used?
Then we will be seeing about Lantern Community, right?
Uh, we will also get to know about this, uh, what exactly what kind of functionalities is specifically
in Lantern Community.
Then we will be seeing something called as lantern tools.
Right.
And there are many more important modules that we are going to use in this entire ecosystem.
But at the end of the day, you will be understanding some amazing ways of creating generative AI applications.
And this will be not just simple projects.
It will be very, very extreme and important projects which will actually help you to handle real world
AI scenario, real world use case scenarios.
Okay, then after probably developing multiple application with the help of link chain, then uh, we
will be going ahead and understanding about some of the deployment techniques.
Now, this deployment techniques that I am going to specifically use, I'm going to make sure that whatever
free resources are available, right.
Resources are available with respect to deployment.
I will be doing that.
Okay.
I will not take any paid kind of things with respect to this particular deployment techniques.
Okay.
So all these applications also I will talk about it guys.
We will be using Streamlit to probably develop this entire application.
Streamlit is another framework that we specifically use right.
So here also we are going to specifically use this Streamlit Streamlit library okay.
Now after covering deployment techniques uh, obviously we also need to know about fine tuning techniques.
So in this fine tuning techniques we are going to see some of the tuning things, uh fine tuning techniques
here.
Uh, we will be specifically using Google Colab.
Again.
We will try to just use free resources and try to do this.
Okay.
Uh, one of the paid LM model will be OpenAI.
Okay.
And here I will also talk about what?
Uh, uh, LM models.
We are specifically going to work.
Okay.
So over here we are just going to use Google Colab, and we are going to learn about some of the important
concepts and techniques.
Uh, like quantization.
Quantization.
We are going to learn about Laura Clara.
And with the help of this, we are going to fine tune, fine tune with some open source LM models.
Open source LM models.
Okay.
Open source LM models.
Okay, so we are also going to cover this so that you also get some experience with respect to the fine
tuning things.
Okay.
Now coming to the sixth topic, uh, sixth important module, which we are basically going to go ahead
and create, you know, it is working with Gen I in AWS, right?
So let's say if you clearly want to completely work or develop your generative AI applications in AWS
by using AWS bedrock.
So there is, uh, there is a service in AWS bedrock which will have many, many open source models
that will be available over there in the hosted mode.
Right?
So with the help of this AWS bedrock and using other important services that are present in AWS, like
uh, Lambda function okay.
So lambda function, then you have API gateway.
So here we will be developing some amazing end to end projects.
End to end projects.
And we will also see how we can deploy this entirely in our AWS SageMaker.
Right?
AWS SageMaker.
So this will be one another amazing module that we are going to work with.
Okay.
In the recent days, uh, one more module is becoming very much popular that is called as Nvidia Nim.
Okay.
Nvidia Nim.
Now Nvidia name uh, is just like, uh, you have this this Nvidia name actually helps you to deploy
the generative AI models in the cloud.
And here with the help of Nvidia name, we will be seeing what all kind of open source models are already
available in this, uh, hosted platform itself.
And with the help of this also we will be trying to create some end to end projects.
And here also we are going to use Lang Chain.
Okay.
We are specifically going to use Lang Chain.
So as you know as this is 2024 we definitely need to new all the new techniques that are available in
the market which will be definitely helpful for you.
Okay.
And finally, uh, I'm also going to cover one more amazing library, which is called as Crew Eye Crew.
I actually helps you to create multi eye agents.
Multi agents, which helps you to solve different kind of which helps you to develop different kind
of generative AI applications.
And this also we will try to use long chain and we'll develop this okay.
Along with this particular AI framework okay I think over here Lange chain will not be used but it has
its own framework.
So with the help of this also we will be generating some kind of amazing applications.
Uh, so overall this is the entire course curriculum which I have actually written step by step how
we are going to cover.
But I feel after doing this many things, uh, you should definitely be able to crack any kind of jobs
and even open your own startup.
Specifically working with any kind of models that you really want to work with right here.
If I just consider the LM models that I am actually going to use, as I said, LM and multi model.
Okay, as I said, I'm going to use both paid and open source so that you get an idea with respect to
both paid and open source.
The first one I'm going to specifically use open AI.
OpenAI.
In OpenAI, I will be showing you examples with uh, with like GPT four.
All right.
We will be seeing with respect to GPT four turbo, we'll be seeing some OpenAI embedding techniques,
OpenAI embedding techniques.
Right.
When I go to open source model, I will be specifically using this Google Gamma model.
So I will be using this Google Gamma model.
Gamma two model.
Right.
Along with this, you'll be seeing the third important open source model that I will be using will be
from meta.
So obviously models like llama three I'll be using it from here.
Along with that and Mistral.
So if you know about the company called as anthropic right.
We're we're also going to use this particular model.
That is nothing but Mistral from anthropic, which is again an open source model that is available.
One of the version of this Mistral, uh, we will be using to solve some of the problems.
Right.
And uh, along with this, uh, uh, you know, in Meta Llama three and we are also going to use something
called as code llama.
Okay.
Code llama is specifically used for creating code assistant.
And.
All right.
So GPT four or GPT four turbo gamma model this model.
And then finally obviously we are also going to use hugging face.
How can I miss this.
Right.
So there are a lot of models uh LM models that are specifically available both open source and paid
in the hugging face.
So we are going to specifically use this.
And at the end of the day, by using all this kind of models, we are going to probably create a lot
of generative AI applications.
I will also introduce you how you can actually use this entire open source model with the help of grok
AI infrastructure, which is important for everyone.
Grok infrastructure.
Right.
Um, grok is an LPO engine.
Okay.
We will talk about it.
What exactly is this LPO engine and how it is better than the GPUs?
Okay.
So overall this is the entire idea of the entire curriculum itself.
There are many things that needs to be discussed, and you will be seeing that as you go ahead.
And all these sessions are like completely in depth, explained.
Um, every project is developed in such a way that you should be able to implement it.
I've written each and every line of code as we go ahead.
Right.
So I hope, uh, you like this particular video.
I will see you all in the next video.
Thank you.
Take care.
Bye bye.