Generative AI Full Course – Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More

this massive course about foundational

generative AI was originally recorded

live we've put it all together into this

one video this course offers insights

into generative models and different

Frameworks investigating the production

of text and visual material produced by

AI the course is taught by three

experienced instructors okay I think uh

we can start with the session so hello

everyone good afternoon to all uh this

is the very first session for the uh

generative AI uh from today onwards we

are going to start with a community

session of generative AI so uh yes in

today's session we'll be talking about

that what all thing we are going to

discuss uh throughout the sessions and

uh this session actually it will be

happening for uh upcoming two weeks and

uh it will be on the same time I'm going

to take the session from uh 3:00 p.m.

onwards uh maybe 3: to 5 so here in this

uh committee session we'll try to to

discuss many more thing regarding the

generative AI so we'll start from very

basic and we'll go to the advanc there

we'll try to develop different different

type of applications as well uh first of

all I will start with the theory uh so

there I will uh discuss about the

theoretical uh uh like stuff and all

that what is generative AI what is a llm

and after that I will uh like go with

the open a Lang and don't worry each and

everything I will discuss in a very

detailed way and and uh I will show you

the dashboard as well where all the

lectures and all will be uploaded and

apart from that I will uh show you the

uh like uh where you can find out all

the videos quizzes and uh assignments

and all because along with the session I

will give you the different different

assignment different different quizzes

so at least you can practice with the

concepts got it guys yes or no so are

you excited please do let me know in the

chat if you are excited

then

great so I think we going to start and

uh so first of all guys what I will uh

do I will give you the uh the detail

introduction of the course that what all

think we are going to discuss in this

committee session and uh here basically

this is our dashboard so let me share

this link with all of you so uh don't

worry my team will share the link of

this dashboard in the chat so from there

what you can do you can enroll it is

completely free you no need to pay

anything for this uh committee session

and all the lectures and all the

assignment and quizzes will be uploaded

over here don't worry I will come to the

curriculum also so here first of all let

me show you the homepage uh this is the

homepage guys uh this is the homepage of

this dashboard and there uh uh like you

can enroll in this particular dashboard

and don't worry uh each and every video

you will find out over the Inon YouTube

channel as well so all the recorded

video and all it will be available

inside the Inon YouTube channel uh

definitely this video is going to be

record after the session so this video

will be available inside the I YouTube

channel as well as uh over the dashboard

so here guys this is the dashboard I

think you got a link of the dashboard so

there you can enroll and then uh like

you can start your journey of the

generative AI so here guys uh me and buy

both are going to take this particular

session so there we are going to discuss

in depth uh about the generative AI

about the llm we'll try to discuss about

a various application various recent

model llm models and all uh we have so

many things uh to discuss about the

generative AI we have planned for the

two weeks but uh maybe uh more than 2

weeks uh we'll try to uh take if we are

not able to cover and like all the

curriculum whatever we have defined

whatever we have thought uh so

definitely we'll be extending the date

as well but yeah we will make sure that

within 2 week whatever curriculum we

have defined we'll try to complete it so

here guys are me and uh buy so if you

don't know about me guys so my name is

Sun my name is s Savita I'm working in

Aon from past three year and I have I'm

having an expertise in data science uh I

have explored U every aspect of the data

science like machine learning deep

learning and advanced deep learning like

computer vision NLP I have worked with

the mlops as well uh there I have

designed a various applications and all

got it so yeah uh you can search me

about more over the LinkedIn uh there

you will get uh you will you will get

got my profile and you will uh like uh

get each and everything in a detailed

way so here guys uh what you need to do

so first of all you need to enroll to

this particular dashboard uh you no need

to pay anything over here and if you are

going to login after login what you need

to do uh so uh you will be redirect

redirecting to this uh particular

dashboard and uh so let me show you the

dashboard first of all this is the

dashboard guys as of now there is no

such videos and all uh definitely after

the session we'll uh upload the videos

and assignment and quizzes so definitely

along with the sessions and all you can

practice so this thing is clear to all

of you yes or no have you enroll uh

through this dashboard did you get this

Dashboard please do confirm in the chat

I'm waiting for your reply please do it

guys great so I can see uh uh many

people are saying yes so definitely now

uh we can discuss about the curriculums

and all so whatever thing we are going

to discuss throughout this committee

session first of all I will give you the

detail uh like uh detail introduction of

the uh syllabus uh what all topics we

have uh we'll try to more focus on the

re recent Trends I'm not going into the

uh classical uh machine learning and the

Deep learning basically so I will focus

more Focus basically on the open Ai

langen and all so don't worry

I will give you the detail introduction

of the syllabus uh that whatever thing

we are going to discuss inside this

committee session so the first thing

what you need to do guys you need to

enroll inside this dashboard and uh

whatever like videos and all you will

get uh you will like go through and

basically you can watch it over here

itself directly now let's discuss about

the curriculum and all that what all

thing we are going to discuss and uh for

that basically I have created one PPT so

let me show you that particular p PPT uh

just a

second so here is a PPT guys can you see

this PPT yes or no please do confirm in

the chat if it is visible to all of

you

great I think uh this uh PP is visible

to all of you now guys uh we can discuss

uh that uh what will be our uh like

topics and all uh that what all thing

basically we have to discuss throughout

this committee session so here uh first

of all I will start from the generative

AI so there I will give you the detail

overview of the generative AI that what

is a generative AI uh why uh we should

use a generative AI uh what all type of

like application we can create and uh

each and everything each and every every

theoretical stuff we try to discuss

regarding the generative Ai and after

that after the generative AI I will come

to this uh large language model so just

a second let me open my uh pen as well

so I can write it down um each and

everything

yeah so here guys uh we can uh I can

write it down as well now so here the

first thing basically first I'm going to

start from the generative AI there uh

definitely I'll will be talking about

each and everything each and every

aspect of the generative AI then after

that I will come to this large language

model there I will try to discuss this

llms large language model in a very

detailed way we'll try to see the

complete history of the large language

model that what is a large language

model what types of model we have what

was the classical model and uh what is a

recent model okay so each and everything

we'll try to discuss regarding this llms

and after that after completing this uh

theoretical part theoretical of this uh

stuff and all regarding the generative

AI regarding this llms I will come to

this open AI open Ai and this lenen so

there uh we'll try to discuss in a very

detailed way that what is the open AI

what is the open AI API and inside the

open AI API we have a different

different them right so in OPI openi

itself you will find out a various model

that like openi open has created a

various model uh that different

different version of the gpts okay so it

is having some uh like a old model as

well some legacies and all and some

upcoming models so each and every model

will try to discuss I will give you the

walk through regarding those particular

model and I will discuss about the

python API python uh API uh that uh how

you can uh use utilize those particular

model by using the python getting my

point and apart from that apart from the

python API and all will try to discuss

that uh like if we are going to be uh if

we are going to use the Lenin right so

how it is different from the open AI so

at the first place I will give you the

uh detailed differences between this

open Ai and this lenen that how it is

different to each other that why this

Lenin is required then I will come to

the Lenin and then again we'll try to

create uh again basically we'll try to

Define the lenen and all uh by using the

python we'll try to uh use the lenen or

different different like a component of

the Lenin like memory chain agents and

all and yes after that I will try to

create one

application okay so here uh basically

we'll try to create one application and

with that uh definitely we'll be able to

justify the knowledge whatever actually

uh we are going to learn regarding this

llm open lenion by using uh that by

creating that particular project

and after that I will uh come to the

advanced part Advanced part like uh

Vector databases uh we'll try to discuss

about a different different Vector

databases and first of all we'll try to

discuss the need of the vector database

that why it is required what is the

meaning of the embedding uh how we can

uh like uh save the embedding how we can

retrieve that and how this a vector

database this a vector database plays an

important role whenever we are going to

create any application related to this

llms okay so there we'll try to discuss

about the vector databases and then I

will come to the some open source model

so uh first of all I will come to this

uh llama uh and I will discuss about the

Llama indexes what is the Llama index

and we have a like couple of Open Source

model which is a very very famous so

we'll try to talk about those model as

well like we have a llama to itself we

have a falcon we have a bloom there are

various model and we'll show You by

using those model how you can create uh

like a how you can create your end to

and application uh you can solve any

sort of a task just take a name don't

worry I will give you the detail

overview about the NLP and all that what

all task does exist what all task

basically we have what all task we can

solve by using this llm each and

everything we'll talk about and then

finally we'll create one more end to

endend project there we'll try to uh use

the entire knowledge uh whatever we are

going to be learn uh like this Vector

databases different different open

source model and a length CH open a

llama indexes and finally we'll try to

deploy that model by using the amops

concept so did you uh like this syllabus

yes or no please do let me know guys

please do let me know in the chat if you

like the syllabus yes or

no the agenda is clear to all of you if

you can write it down the chat I think

uh that would be

great great so I can see many yes in the

chat and many people are saying yes they

are

able to get yes don't worry we'll give

you the PPT and all each and everything

will be there in a uh resource section

so from there you can download this PP

you can download this entire thing

whatever I'm uh like I will be using

throughout the

session yes uh so fine I got a

confirmation now uh the first thing uh

many people are asking the prerequisite

what will be the prerequisite if you are

starting with this committee session so

prerequisite wise uh if you have a basic

knowledge of the Python if you have a

basic knowledge of the Python if you

know about the core python uh in a core

python actually we have uh uh like a

ifls for Loop and uh different different

type of data structure and the knowledge

of the database exception handling if

you are uh if you know about the basic

python the basics of python then

definitely you can proceed with this go

along with that if you have a like some

basic knowledge about machine learning

and deep learning so you will understand

uh the concept basically whatever uh

like we are going to teach you in a

better manner in a better way because

here I'm not going to talk about the

classical uh ml or the basics of the

deep learning like uh artificial NE

Network CNN and all definitely I will

give you the overview about the transfer

learning fine tuning and all but here uh

uh I won't talk about the neural network

and this recurr neural network lstms and

all so uh if you have a basic

understanding of machine learning and uh

deep learning so definitely you will

understand the concept in a very well

manner otherwise basic python knowledge

is fine for creating application basic

python knowledge is uh like fine okay so

you no need to worry about it uh

whatever thing actually I need to

explain you definitely I will do that uh

in the class itself and uh we'll do the

live implementation I'm not going to

show you any uh pre-written code and all

uh definitely I will write it down each

and everything in front of you only got

it so prerequisite is clear so

prerequisite nothing just a python or uh

I can write it down over here basic

knowledge basic knowledge of ML and DL

if you know this much then definitely uh

like uh you will be able to understand

each and everything in a well

manner great so yes we'll talk about the

RG approaches and all each and

everything diffusion model is there

there are some recent model in l each

and everything will talk about and you

will be capable so uh let's say if you

are working in your company or maybe you

are trying to switch into the generative

AI or maybe you are fresher in every uh

case right so this community course will

help you definitely if you if you are

going to attend every session if you are

going to learn along with me definitely

you can build anything after learning

all sort of a thing got it great so I

think uh this uh introduction is clear

to all of you now I already discussed

about uh about the dashboard and all so

uh I given you the walk through of the

dashboard so link you can find it out

inside the chat inside the chat and from

there itself you can enroll now the

syllabus is clear dashboard is clear

each and everything is fine so I think

we can start with the introduction of

generative AI generative Ai and llm

because from today's on uh from

tomorrow's onwards I I will be like move

to the Practical part and there I will

be talking about the open a how to

generate a open key how to use the open

API and uh we'll try to understand the

chat completion API functional API and

uh we'll try to understand the concept

of the token also that what is a token

uh like how many token should I use

whenever we are giving any sort of a

prompt what is a different different

prompt template and all there are lots

of thing which we need to understand so

today's session actually it will be uh

like completely introduction session and

in this particular session uh we'll talk

about the generative Ai and the history

of the large language model so guys uh

are you ready can I uh get a quick yes

in the chat if you are ready

then yeah definitely we'll talk about

the uh like use cases of the generative

and all u in today's session itself I

will like give you that uh particular

idea that where you can uh utilize this

generative AI in a real time

yes definitely this course content and

all whatever you are seeing over here

this one definitely it will be available

over the dashboard as well so this is

our dashboard we'll update it over here

inside the course syllabus section so

each and everything uh like uh we'll try

to update in the dashboard

itself here is a class timing and all

and uh I will make sure that the uh the

link also okay so uh we are not going to

uh we are directly streaming over the

YouTube so directly you can uh join

through the YouTube so for that you just

need to subscribe the channel and you

will get a notification in that

case great so people are saying yes sir

we are ready ready ready

great yeah definitely Wy we'll try to

discuss the applications and all and uh

I will explain you all the thing and

that uh specific way only don't

worry we are going to build AI

application by using the AI

tools AI based

application great so I got uh many yes

in the chat now I think uh we can start

with

a uh with the introduction of generative

Ai and the LM so guys uh first of all

tell me that how many of you you are uh

you have started with the generative Ai

and all already means uh you have

learned something at least you have

learned the basics in all uh so if you

can write down the chat so that would be

great means you are starting from very U

like a scratch or you have some sort of

idea

great so many people are saying uh so

some people are saying they know about

the basics and some people are saying uh

they're starting from the scratch don't

worry so uh basically I will start from

the scratch only now here guys uh you

can see I have created One PP for all of

you so let me uh first of all let me go

through with this particular PP and

later on what I will do I will again I

will give you the revision by using this

PP only and in between I will use my

Blackboard also for explaining you some

uh Concepts and all so here is my

Blackboard so here I will be writing

down uh the whatever basically thing I

need to explain you and in between I

will be using the ppds and all so first

of all let me go through this particular

PP and here you can see so uh I have

written some sort of a name uh so in the

generative AI whenever we are talking

about the generative AI or a large

language model so couple of name are

very famous nowadays and in in those

name actually this chat GPT is a uh like

very very famous so here I have written

this chat GPT it's a product of the open

AI as we know about this Chad GPT

everyone knows about the chat GPT yes or

no I think yes now if we talking about

this Google bar so it's a product of the

Google and we talking about this meta

llm 2 so this meta llm 2 it's a product

of the Facebook got it guys yes or no so

yes uh nowadays actually everyone using

this chat GPT Google B meta lm2 is it

it's also a platform similar to this

chat GPT uh where you can uh chat or

where you can ask a specific question

which you uh which you do in a chat GPD

itself So Meta lm2 it's a a model from

the Facebook site now here guys if we

are talking about the generative AI or

we are talking about the large language

model so in our mind the first image

which comes into the picture that is a

chat GPT Google B and meta llm 2 yes or

no tell me guys yes because of that only

uh because of this chat GPT Google B and

like the other the different like

whatever application you are seeing

nowadays right so mid journey is one of

the application or maybe Delhi uh or

different different application because

of that only I think you are learning

this uh particular uh thing this

particular course this generative AI

course yes or

no yes so but guys this generative AI is

having their own Roots it's not all

about the chat jpt Google B and some

other uh application which you are

seeing chat jpt is just a application of

this generative AI chat GPT or this

Google B is just a application of this

uh like llm large language model

basically we are using this large

language model in a back end U like

whatever application you are seeing like

chat GPT and all in the back end but

apart from this this generative Ai and

this llm is having their own Roots so

first of all what I will do I will

explain you the concept of the uh like

first of all I will uh start from the

deep learning itself means I need to

explain you few uh terms and terminology

regarding this deep learning so let me

uh back to the Blackboard so there I

will be talking about the basics of the

deep learning So within uh 5 to 10

minutes I will be discussing the types

of the neural network and all and then I

will directly move to the uh like LMS

and this uh genem so here guys uh you

can see uh what I can do I can uh draw

one box over here so this is the uh you

can think this is what this is the

neural uh uh basically if we are talking

about the okay so first of all let me

start from the deep learning itself so

uh if we talking about a deep learning

so uh we can uh divide this deep

learning into three major segments so

let me write it down over here this a

deep

learning so guys this deep learning

actually we can divide into three major

topic so the first topic actually which

is called artificial neural

network artificial neural network netork

the second topic is called convolution

neural network

CNN the third one basically which is

called recurrent neural network so we

have a three types of the neural network

and we can divide this a deep learning

into this three major section apart from

this you will find out other uh like

topics as well so let me write down

those uh thing over here so the fourth

one uh which I can write it down over

here that is a uh reinforcement learning

and uh the fifth one we generally talk

about it uh so that is what that is a

gain so this gain also it comes under

this generative AI I will talk about it

I will talk about this game I will like

give you the glimpse of this uh

generative advisal Network that what is

this and how the architectures look like

of this gains and why I'm saying that

this gains comes into the generative AI

so if we talking about thisn so let me

draw the box now so if we are talking

about this n so here guys see we have an

input layer inside this Ann actually

what we have we have a input layer and

uh you will find out the output layer

and in between actually in between this

input and output we have a hidden layers

so just a wait now over here guys see we

have a input layer and we have a output

layer now in between actually you will

find out a hidden layers various hidden

layer so let me write it down over here

input and here you'll find out the

output now here in between this input

and output you will find out of various

hidden layers so let me write it down

the hidden over here so this hidden

layer actually it is nothing it's a

hyper parameter so we can have as many

as hid layer we can have as many as node

inside the hidden layer we all know

about the artificial neural network I'm

assuming that thing now if we talking

about this uh CNN actually so the CNN is

nothing so in the CNN uh one more thing

you will find out in terms of this CNN

that is what that is a convolution we

always perform the convolution in terms

of this CNN so here if we are talking

about this enn so uh we are using the uh

like structure data where we have a like

different different features numeric

feature or categorical feature and uh we

try to solve the regression and

classification related problem but

whenever we are talking about this CNN

so here uh the CNN actually specifically

we use for the image uh related data

image or video related data you can say

that uh we use the CNN and all for the

grid type of data okay so we use the CNN

for the grid type of data and there uh

like you will find out one more

component that is what that is a

convolution so here uh let me write it

down so the component name is what

component name is a convolution so in

the convolution actually you will find

out a various step so uh we have a

various step in the convolution itself

so the very first step which we perform

what we do guys tell me we perform the

feature X section by using a different

different filter after that what we do

we perform the pooling and then we

flatten the layer so there are different

different like uh uh steps you will find

out inside the convolution itself and

after that what we do we apply the fully

connected layer so that is nothing that

is my Ann itself so over here I can

write it down we have this convolution

and we have a artificial neural network

so this is my first architecture which

is a like uh which is the Ann itself and

and this is my second one that is what

that is a CNN now if we talking about

the third one which is a very very

interesting that is called recurrent

neural network that is called recurrent

neural network so this enn we generally

use for the structured data where we

have a numerical column or categorical

column and in the Target column like it

will be a numeric or categorical one and

based on that basically we are going to

decide whether it will be a

classification problem or a regression

problem now if we talking about this CNN

so already I told you if you're talking

about this RNN so the name is what the

full form what the full form is the

recurrent neural network so this RNN

actually we are using for the sequence

related data so wherever we have a

sequence wherever we have a sequence so

this RNN we used for the sequence

related data now let me do one thing let

me draw the architecture of this RNN so

over here guys in the RNN what you will

find out so let's say this is my uh box

and here is what here is my input so

this is what guys tell me this is my

input now here is what here is my output

so let me draw the output one more time

this is what this is my output got it

now here guys see uh this is my input

this is my output and this is what this

is my hidden layer now in the hidden

layer actually you will find out one

thing one concept and the concept is

nothing the concept is called a feedback

loop okay so whatever output I'm getting

from the hidden layer actually again we

are passing that output to hidden layer

until the entire time stem so that thing

actually uh we learn or we learn in in

the RN itself actually this RNN is

nothing it's a special type of neural

network and there you will find out the

feedback loop feedback loop means what

so whatever output we are getting from

the hidden layer again we are passing

the same output to the hidden layer

until we are not going to complete the

entire time stem that is what that is

the RNN now uh you are uh we are talking

about the llm so why we are uh why I'm

discussing this RNN and all because this

llm actually somehow it is connected to

this RNN itself before starting with a

llm a large language model we'll have to

understand the concept of the RNN lstm

attention uh like encoder decoder and

then attention self attention and all so

here I'm not going to discuss in a very

detailed way I'm just giving you the

glimpse of that that what is a like RNN

what is the lstm what the Gru and then

what was the sequence to sequence

mapping and where this attention comes

into a picture then how they have

invented the self attention then how

they started the using this transfer

learning and this finetuning in terms of

this uh in terms of this large language

model why we are calling it is a large

language model why we are not calling it

a model okay so each and everything

we'll try to discuss now uh you all know

about this uh reinforcement learning and

all so in the reinforcement learning uh

you will find out one agent environment

regarding that particular agent you will

find out a different different state

getting my point and then you will find

out the feedback so that actually it

comes inside the reinforcement learning

and that is also part of the deep

learning only now if we are talking

about gain so gain is uh nothing

actually so in the gain again you will

find out a neural network uh which we

are using for generating a data and that

also comes in under inside the

generative Ai and we have a different

different types of game so first of all

tell me guys uh this uh uh like types of

the neural network this is clear to all

of you please do let me know in the chat

if uh this thing is clear then uh I will

proceed with the next

topic use cases wise I will come to the

use case and I will uh try to discuss a

different different use case I will come

to the use case then I will tell you the

applications of that and then I will

come to the domains as well then in what

all domains you can apply those use

cases so don't worry each and everything

we'll try to discuss over here

yes I will directly come to the

generative a itself but before that I

will give you the timeline don't worry

from Tomorrow onwards I'm going to be

start uh I'm going to start from the uh

like uh from the open ey itself uh like

complete practical and all so no need to

worry about it s Prasad I think you got

your

answer

I think uh this basic introduction is

clear

now yes coming to the generative only

don't

worry yeah it's going to end to end uh

we'll try to discuss end to end thing

don't worry about

it if you have any questions and all so

you can directly ping into the chat

uh so I will reply to you don't

worry okay so I think now we can proceed

so guys here uh in the uh PP itself I

was talking about the generative AI then

I given you the uh like uh uh the types

of the neural network and I just explain

you the uh like the regarding the

artificial neural network and the CNN

and this RNN so here in the generative

AI uh you'll find out that I have like

included a few slides and all so let's

try to understand a few uh thing from

here and then again we'll go back to the

uh the Blackboard and then I will try to

discuss few more concept so over here uh

we have seen the chat GPT like I was

talking about the different different

application like chat GPT Google B and

metm and all now let's talk about the

generative AI that what is a generative

AI now here you can see the definition

of the generative AI uh which I have

written over here uh that is what that a

generative AI generate new data based on

a training sample right so the name is

uh the name is self-explanatory right so

the name is explaining everything

generative AI the AI which is generating

something now what all thing we can

generate so here if we are talking about

the generative AI so you can generate

images you can generate text you can

generate audios you can generate videos

as a output you can generate anything uh

so uh this image text audio video it's

nothing it's a type of the unstructured

data and definitely it is possible by

using the generated AI we can uh

generate this type of data by using the

generative AI now if we are talking

about the generative AI so uh as I told

you that it is having their own Roots

okay so it is having their own roots and

if we are going to divide this

generative AI so we can divide into two

segment so the first segment is called

generative image model and the second

segment is called generative language

model and this llm actually it falls

into this particular segment into this

generative language

model are you getting my point yes or no

I think yes so if we talking about this

generative image model so I told you

when I I was talking about the Deep

learning uh like Ann RNN and CNN

reinforcement learning and there was a

gain so initially we were using the gain

for generating a data so let me show you

the architecture of the gain so the how

the architecture of the game looks like

so with that you will get some sort of

idea in the game we are using this uh

neural network only so let me show you

the architecture of the game so let me

search it over here over the Google game

architecture now uh here in the image uh

let me open the architecture of the game

so here guys uh just see so in the gain

actually we have two main components so

the first component is a generator this

is what this is a generator okay so uh I

think this is visible to all of you this

is what this is a generator and here you

will find out discriminator so this

generator and discriminator is nothing

it's a neural network so we are passing

this real data so here basically what we

are going to do we are going to pass a

real data and here we have a generator

which is generating some sort of a uh

like synthetic data and here we have a

discriminator based on that we are going

to discriminate between real data and

the synthetic data so this is the

architecture of the game and inside this

architecture you will find out we are

using two main thing we are using two

main component the first one is

generator and the second component is a

discriminator I think you're getting my

point and this generator and

discriminator is nothing it's a neural

network got it so this is also comes

under this generative AI so now let me

show you this generative AI now over

here I have written two points

generative image model and generative

language model so if you're talking

about generative image model so in our

previous days in our back back days

actually in our old days in 2019 18 so

this gain was very popular for

generating a data again uh this gain is

very uh like EXP uh like expensive in uh

terms of computation power and all so it

is very like very much expens uh like

expensive in terms of like uh

computation uh so over here you can see

so we were using this gain uh we were

using this gain for generating images

and all in our back days in 2018 and in

2019 and it was very very popular and we

have a different different uh variants

of the Gams if you'll find out the type

of the gain you will find out many types

now uh recently actually you will you

have find out the trend of the llm large

language model now guys here uh we are

uh we are talking about the game and

then uh this gain basically it was the

old concept it is a old concept

basically and there are different

different variants of the gain as well

now over here if we are talking about

this large language model so it become

very famous from the Transformer I will

come to the Transformer I will tell you

the complete history of the Transformer

as well now uh this image model and this

language model

but a recent days in a recent days what

I have seen in terms of this llm and all

even we can generate the images by using

this llm we got those llm basically

which is like that much powerful so by

using those particular llm we can

generate generat images as well okay so

we I will show you couple of models and

all uh regarding this uh like image

generation and definitely you will get

some sort of idea that how the uh those

part particular alms is working in terms

of image generation I can give you a

couple of example uh like Delhi so Delhi

is a example you can uh check over the

open a which is a model which is like a

uh like a famous for the image

generation now here uh if we are talking

about this image model so actually see

this image model basically it was

working for image to image Generation

image to image generation now this

generative model actually so if we are

talking about this generative model it

is working Tech uh it is working in

terms of text to image generation text

to image generation and text to text

generation so this two tasks definitely

we can perform by using this llm model

and this image to image generation

before we were doing it by using this

gain model in 2018 and in 2019 now uh as

I told you that we have those powerful

model in our recent Days by using those

particular model definitely we can

Implement image to image generation as

well that is also possible so uh

regarding that uh definitely I will show

you couple of model so we are having

four tasks here I have written it now

let me move to the next slide and let me

show you that what I have so here guys

you can see uh this cat is representing

a genitive model where you are giving a

prompt uh means where you are giving a

question and uh as a response U again as

a output basically you are getting a

response so in terms of uh see here we

are talking about generative model I'm

not talking about specifically this El M

okay so I told you this uh generative

model actually uh you can think it's a

super set this generative Ai and under

this generative AI you will find out

this llm and gang is also part of the

generative AI getting my point I think

this thing is getting clear so over here

we are talking about the generative

model so we are giving a input and we

are getting a like output now

specifically if I'm talking about

regarding this llm regarding this large

language model so this input actually

this is called input prompt and the

output actually it is called output

prompt so this cat you can imagine as a

generative model or as a llm model so

what we are passing as a input we are

passing input prompt and we are getting

as a output output prompt so this prompt

term is a very very important I think

you have heard about this uh prompt

engineering and all that uh uh like uh

prompt engineer is getting this much

that much and this prompt engineer plays

a very important role if uh we have to

design any sort of of a prompt now uh

different different types of prompt of

like zero short prompt few short prompt

few short learning and all we'll talk

about it as I will progress with the

like implementation and all in between I

will give you like idea regarding each

and everything now over here guys you

can see uh where this generative AI

exist so if you will look into the uh

look into through this particular slide

so here you will find out this

generative AI actually it lies inside

the Deep learning getting my point so

the generative AI actually it uh like

reside inside the Deep learning uh

initially only I have explained you that

uh we have a different different types

of neural network and it's a part of the

deep learning only now whether we are

going to generate an images by using the

llm or by using the gains or whether I'm

going to perform text to text generation

text to image generation or image to

text Generation by using the llm both

lies inside this generative Ai and this

generative AI is a part of the it's a

part of the tell me it's a part of the

deep learning now over here guys I have

written a couple of more slid so I will

try to explain you uh but first of all

let me give you the timeline of the llm

and then I specifically I will come to

the llm and all and I will be talking

about this discriminative Ai and the

generative AI as well so tell me guys uh

this part is clear are we going good are

you able to understand whatever I'm

explaining to all of you so if you are

getting it so please write down the chat

and you can ask me the questions as

well

if you have any uh type of Doubt or U

like if you're getting it or not getting

it whatever you can ask me in the chat

uh like chat section uh I will reply to

your questions

no reinforcement learning is not

required uh uh specifically we should

not go for the reinforcement learning

and

all yes this is a part of the uh like

this Genera way is a part of the deep

learning right

right yes llm model used in a generative

AI correct you got it uh

guys mathematical intuition so we will

talk about the mathematical intuition

and all but this uh more uh this course

this comp session is it is more focusing

on the applied side so I will create a

various application in between whatever

math iCal concept and all will be

required I will let you know that don't

worry great so I think uh people are

getting it and uh they are trying to

understand fine so whatever I have

explained you let me explain you the

like Blackboard uh and then again I will

come to this PP and we'll try to uh wrap

up the theoretical stuff and U then I

will explain you the applications and

all so over here guys see I was talking

about this Uhn CNN RNN RL and G now I

started from the generative AI itself so

I have started from the generative Ai

and I told you this generative AI is

nothing you can consider it as a super

set as a super set now inside this

generative AI you will find out many uh

like uh many uh concept many topics and

all so here uh regarding the generative

AI there is uh two main thing which you

will find out the first one is

gain gain that is a generative aders

Network the second is what

llms llms large language model now we

have a various task so here let me write

down the task as well so the task wise

so here I told you the different

different task basically so the first

task which I can write it down over here

that is a image to image Generation

image to image

generation now the second task was the

uh image to text uh text to text

generation text to text generation text

to text generation now the third task

was

the uh image to text

Generation image to text

generation and the fourth one was

the uh image to image generation sorry

uh text to text Generation image to text

and text to image generation so let me

write it down over here text to image

generation text to image

generation now if we are talking about

this image to image generation yes we

were able to do this particular thing by

using this

gain we have seen the gains now we are

talking about this text to text

generation yes it was possible by using

the lstm RNN and the uh different

different by using the different

different model as well but yeah this

text to text generation actually

nowadays you are seeing uh we are

preferring this large language model for

this text to text generation and you

this chat GPT is a biggest application

uh biggest like example for that the

chat GPD which we are seeing image to

text generation yes uh this is also

possible by using a different different

model like RNN lstm and Gru image

captioning if you have if you uh if you

have heard about this uh like image

capturing task so that is also possible

uh by using this uh like uh classical

model but yeah by using this llm also we

can perform it we can uh do it now if we

are talking about this uh text to image

generation so yes uh this type of task

nowadays it is possible by using the uh

llm so yes yes uh llm is able to do llm

is able to perform a various amount of

task uh whether it's a homogeneous or

it's a hetrogeneous now uh I was talking

about the uh llm uh sorry I was talking

about this generative AI so where it

exists so this generative AI actually it

exists uh in a u like a deep learning

itself so you can think that AI is a

superet machine learning is a subset

deep learning again is a subset of the

machine learning and this generative AI

is a subset of the deep learning because

as I already told you we have a

different different uh like other neural

network also in a uh like a deep

learning and this CNN is one of them

this a convolution neural network okay

so I think this part is clear to all of

you now let me draw the architecture uh

that where this uh generative AI exists

so you can think this is what this is my

AI this is one uh this is the like a

super set now here this is what this is

my machine learning this one now uh

inside that you will find out the uh

deep learning and inside the Deep

learning you will find out this

generative AI uh so let me take a

different color over here uh let me take

uh this color so here you will find out

the generative AI so this is what this

four circle is what this is the

generative AI got it now here uh you can

see why we are saying so why we are

saying this is a like a subset so I

think each and every explanation I have

given you over here uh you can uh prefer

this uh like a this particular slide

that why I'm saying this generative AI

is a subset of the deep learning so let

me write it down over here this is what

this is nothing this is a gen Ai and

it's a subset of the deep learning now

guys let me explain you the timeline of

the uh this llm so uh now you got it

that this uh llm is nothing it's a part

of the generative a itself this large

language model now let me talk about the

complete timeline of this large language

model so how it evaluate and uh uh I can

like talk about the complete history of

it and uh here guys you can see that

first I was talking about the RNN so as

you know that uh what is the RNN tell me

RNN is nothing it's a type of the neural

network it's a type of the neural

network so uh there basically we have a

feedback loop again we can pass the

information to our H layer now you will

find out a different different types of

RNN or some Advanced architecture in

terms of this RNN itself El the second

uh like thing which is a type of the RN

itself that is called lstm

lstm right so in the lstm actually uh if

we are talking about this lstm so here

you will find out the concept of the

cell state so in the RNN we just have a

Time stem and it is for the shortterm

memory it is for the shortterm

memory we cannot retain a longer

sentences by using this RNN it is not

not possible if our sentence is a very

very huge or it's a very very long so we

cannot retain that particular sentence

by using this RNN but if we are talking

about this lstm yes we can do it by

using this

lstm so in this lstm you will find out

the concept of the cell state so uh this

uh lstm is nothing it is for the

short-term dependency and it is for the

long-term dependency also it is for the

short like a memory short-term memory

and is for the long-term memory as well

if you look into the architecture of the

lstm so you will find out along with

this uh time stem so here we have the

time stem U like it's a hidden State

actually uh like on a different

different time stem along with that

you'll find out one cell state so it is

going to retain it is going to retain

the long-term dependency and in between

in this a time stamp in this short-term

memory and in this cell State you will

find out a connection the connection in

terms of gates so here you will find out

one connection uh like uh one gate

basically that is called forget gate so

here I can write down the forget gate

now here you will find out one more gate

actually so that is called input gate

here you are passing the input now here

you will find out one more gate over

here that is called output gate output

gate okay so we have three gates inside

the lstm for sustaining a long-term

dependency or for reminding a longterm

uh long sentences now uh you will find

out one more updated version of the lstm

so this RNN is a old thing this lstm is

also old thing now you will find out one

more updated version of the lstm that is

what that is a GRU so this Gru actually

they have invented in

2014 and they had they took the

inspiration from the lstm itself now

inside this Gru you won't find out the

concept of the cell State everything is

being done by the hidden State itself

and here basically in the gru we just

have two gate update gate sorry reset

gate and update gate and it's a uh Advan

or you can say it's a updation on top of

this lstm it's a updated version of the

uh like lstm itself now what is the full

form of the gru G and recurrent unit now

over here guys see this was the three

architecture which was very very famous

during 2018 and 19 in our old days now

here see uh one concept comes into the

picture if we are talking about this RNN

lstm and Gru so by using this particular

architecture what we are doing so by

using this particular architecture we

are going to process a sequence data yes

or no we are going to process a sequence

data now here one concept comes into a

picture sequence to sequence mapping and

for that only we are using this

particular architecture so we have a

different different type of uh like a

mapping technique so let me write it

down over here uh different different

type of mapping technique

uh now it is fine uh I think I'm audible

to everyone

now now I am audible guys please do

confirm in the chat I think there were

the issue from the mic side

now I'm audible so please do confirm in

the chat if I'm audible then and uh is

there any Eco or uh what

so guys are you facing any Eco in my

voice now it is

fine yeah it is perfect I

think great fine fine fine uh it's

clear great uh I think now I am audible

to everyone sorry I think there was a

issue from

the do let me know in the chat uh from

where I lost my

voice so this concept is clear this one

to many or one to one one to many many

to one many to

many

[Music]

yeah so I think uh I was there RNN lstm

and Gru now I think it is fine I'm

audible to everyone great so I was

talking about RNN lstm Gru and then U I

talked about the different different

mapping sequen

now uh this mapping sequences actually

we can Implement by using this uh RNN

lstm and

Gru so over here uh yes so 1 to 1 one to

many many to one many to many RN and LSM

and Gru this was the sequences actually

I was talking about now in 2014 actually

see this was the sequences by uh we can

Implement by using this different

different models getting my point now

over here uh if we talking about this

particular sequences definitely we can

uh like uh per we can uh create a

various uh application by using this

model but here basically we are having

some sort of a restriction uh as I told

you the different different application

like one to many many to one so many to

one means uh you can think that

sentiment analysis one to one to many

means what one to many you can say image

capturing many to many image uh sorry uh

language translation so there are

various application of the sequences now

see uh we are talking about the

sequences uh the sequence to sequence

mapping now uh we can definitely

implement it by using this particular

architecture so the problem we were

having the problem was actually uh we

cannot see let's say we are giving an

input in the input actually we have a

five words so whatever output we'll be

getting in the output also we should

have a five words

so it's a fixed length input and

output getting my point what I'm saying

so by using this particular mapping 1 to

one many to one or like many to many

specifically we are talking about many

to many so there was some problem there

was some issue the issue was fixed

length input and output so whatever

number of inputs we are passing in terms

of this many too many I'm talking about

okay so whatever number of inputs we are

passing so those many output on we can

get it over here in the output itself so

uh here actually one a research paper

came into the picture in 2014 you can

search about uh the research paper

sequence to sequence learning so inside

that uh paper they have introduced the

concept of the encoder and decoder in

the encoder and decoder actually the one

segment the one segment was the encoder

segment segment so let me uh draw it

over here so the one segment was the

encoder segment and the another segment

was the decoder segment this another

segment was the decoder segment and in

between actually in between we were

having in between actually we are having

the context Vector so here uh in between

this encoder so we are having the

encoder and we are having the decoder

decoder one part was the encoder and one

part was the decoder and in between we

having the context Vector means whatever

information was there whatever

information was there from encoder to

decoder we are passing through this

context Vector means we are wrapping all

the information in this context vector

and we are passing to the

decoder that actually the paper uh has

been published in 2014 you can search

about it you can search over the Google

sequence to sequence learning so let me

uh search in front of you only now over

here I can write it down sequence to to

sequence

learning research paper now uh over here

guys you will find out this uh

particular research paper now just try

to read this paper now here in this

particular paper they have clarify the

issue that what was the issue with the

classical mapping so that was restricted

to the input and output now over here

you if you will read this particular

research paper so easily you can find it

out the issue here itself in the

like introduction itself they have

mentioned they have mentioned this uh

despite their flexibility and power can

only be applied problem who inputs and

targets can be sensibly encoded with the

vector of fixed dimensionality it was

just for the fixed dimensionality and

basically there was we were having a

limitations so for solving that

particular limitation this a sequence to

sequence learning paper came into the

picture and there was three person Ilia

sasar and orol and this there was one

more person and this paper from the

Google side now here guys uh let me open

this uh Blackboard again so there was a

context Vector but this uh encoder and

decoder also was not able to uh perform

well for the long uh longer sentences so

here in the research basically they have

proved if my sentence is going uh is

going uh like above from 30 to 50 words

right if it is longer than 30 to 50

words so in that case it was not able to

sustain the context it was not able to

sustain the context if we are using this

encoder decoder architecture now you

will ask me sun what we were having

inside the encoder and decoder so we are

talking about the encoder so again here

we were using the either RNN lstm or uh

lstm and we were using this Gru and here

also in the decoder also we are using

this rnl we are using the lstm and we

were using this

Gru got

it I think you got the problem now and

you got to know about the encoder and

decoder so we have started from the RNN

then now we came to the lsdm Gru and

then we have a different different

mapping and for solving this particular

issue which is related to this many to

many uh like uh mapping many to many

sequence mapping and this uh uh this

language translation is one of the

example if you will search over the

Google translate uh just search over

there anything let's say in the in H you

are saying that or whatsoever so it will

generate output so this input word and

output word will would be a mismatch but

that was a restriction with this uh like

with the classical mapping so for using

this encoder and decoder architecture we

can solve that particular problem now

here also we are having the issue that

we cannot proceed a longer

sentences we cannot proceed a longer

sentences so here One More Concept comes

into a picture inside this context

itself and that was the

attention that was the attention so uh

here neural translation

with just a second let me search about

the neural trans TR a NS relation with

attention yes this was the paper and uh

this was the first paper let me search

about the research paper yeah now guys

uh this was the paper in this particular

paper they have introduced the concept

of the attention and just try to

download it you need to download this

particular paper and uh then you can see

there so just a second let me show you

this paper as well and this is the main

uh like main papers uh basically which

you will find out uh while you will be

learning this deep learning and all so

this paper actually this has been

introduced in

2015 I think in 2015 or 16 now here they

have introduced the concept of the uh

attention actually so just try to read

uh this particular paper at least try to

read the introduction of it uh there we

have uh there they have defined that uh

what was a problem with the encoder and

decoder and where this attention comes

into the picture and what is the actual

meaning of the attention they have

introduced each and everything over here

inside this particular paper inside this

particular paper they have introduced

each and everything regarding the

attention see this is the architecture

of the attention model and uh before

going through with any blog any website

or any tutorial try to uh go through

with the research paper and try to

understand the motive of that research

paper now see guys uh here I'm not going

into the detail of the attention because

this attention itself uh is a longer

topic but I can uh like give you the

glimpse of that that uh uh what what

they were doing in the attention so they

were mapping so let's say we have a five

words in the sentence so they were

trying to map each word whatever word we

have a input we were trying to match

each each input word with the output

word means this input and output this

encoder and decoder if we are talking

about this decoder actually so this is

having the uh information each and every

information of the Hidden State whatever

like in the encoder like you will find

out this RNN lstm or whether it's a GRU

so uh we have a hidden State actually

right so uh this decoder part is having

the information regarding those

particular hidden State all the hidden

State and because of that it was able to

uh it was able to predict so whatever

like sentence and whatever words or

longer sentences or like U like uh the

longer sentences and all which uh

whatever basically we were passing it

was able to predict okay so this word is

related to that particular sentence so

what I will do I will create a like a

one dedicated video on top of it there I

will try to discuss uh this attention

mechanism but yeah here I'm just giving

you the timeline U and with that um you

can clearly understand so uh here we

were having the attention mechanism now

guys by using this attention mechanism

by using this attention mechanism in

2018 Google again Google published one

research paper and the research paper

name was

attention about this encoder in the

encoder and this decoder we were using

what we were using guys tell me we were

using this LSM either we are using the

lstm RNN or maybe Gru now uh there also

we are having the lstm maybe RNN or

maybe you are having the Gru and uh if

we are talking about the attention so

whatever uh information we are passing

from here to here so you are having the

context Vector context Vector now on top

of that we are having the attention

layer attention layer and it was nothing

it was just a mapping from input words

to out output word now here actually

they have published one paper in 2018

and the paper name was attention all

your need attention all your need now

this paper actually it was a

breakthrough in the NLP history this

paper has been published in

2018 and here

actually

decoder but there is one uh there is one

thing basically in terms of this encod

and decoder you won't be able to find

out this lstm RNN and Gru they are not

using any RNN cell any lstm cell or any

Gru cell so here actually they were

using something else and here the what

is a uh name of the research paper so

they were saying that attention all your

need only attention is required for

generating us let's say we are passing

any sort of an input means any longer

input so from that particular input only

attention is required for generating

output now how let me show you

that this Transformer architecture or

let me show you the attention all your

need research paper so attention all

your need research paper so guys this is

a very uh prestigious paper in our NLP

history and uh this changed the complete

history of the NLP and whatever llm and

all whatever you are seeing like

nowadays so they have used this

Transformer architecture as a base model

I will come to that and there I will try

to uh discuss that uh what is the

encoder and decoder again I'm not going

into the depth of the mathematics but

yeah definitely I will try to give you

some sort of a glimpse so over here uh

let me zoom in first of all this paper

and and here guys the paper name was

attention is all your need so this was

the researcher asish Nome Nikki Jacob

you can uh search about these particular

people and here is the abstract uh you

can see and this is the introduction at

least try to read the introduction try

to read this particular background and

the model architecture so this was the

model architecture which has been

introduced by the uh by the Google

researcher and the architecture

basically which you will find out inside

this research paper I think everywhere

you will find out this uh this

particular architecture in uh whatever

NLP tutorial or if you are going to

understand the attention mechanism and

all so this is the architecture now in

this particular architecture let's try

to understand that what all things we

have so see first of all we have a

input okay try try to understand try to

focus over here so we have a input over

here then we have a input embedding so

this is my first thing input and this is

what this is the input embedding the

third thing which we have that is a

positional encoding getting my point and

then after that you will find out the

multi-headed attention then we have a

normal uh normalization and all and then

we have a feed forward Network now guys

just tell me this is what this is a

encoder part this is what this is a

encoder part and this is what guys this

is this is a decoder part this is the

decoder part got it getting my point so

here also we have a two segment first

was the encoder and the second was the

decoder but here we are not using any

RNN cell lstm cell or maybe Gru cell

here actually we are using something

else some other concept and the concept

actually I think this is not a new thing

for you this embedding and all uh this

embedding attention already I talked

about the attention that what it is

mathematically it is having a like uh

like a some different explanation but

yeah I think you got to know the idea

now here we have a feed forward neural

network you know like what is a like

artificial neural network what is a feed

forward neural network so it's not a

like a new thing for all of you and by

assembling all those thing they have

created one cell one architecture and

the name is called this uh Transformer

so this architecture itself is called a

Transformer what is this guys tell me

this is a Transformer now here guys just

see uh this uh Transformer if we are

talking about this Transformer and all

so let me uh tell you few things

regarding this Transformer uh so first

of all guys this is a uh fast compared

to the classical architecture if we are

talking about this RNN lstm and all so

there we are passing the input based on

a Time stem based on a Time stem but if

we are talking about this Transformer

guys so here what is the importance or

what is the like plus point which we

have inside the Transformer it is a

faster why because we can pass the input

in a parallel manner we can pass all the

inputs all the tokens in a parallel

manner in a parall actually we can pass

the input now over here see we have a

input embedding we are doing an

embedding over here and then we have a

positional encoding means we are

arranging a sequence sequence of the

sentence then we have a multi-headed

attention again we are trying to uh

figure out the uh meaning see let's say

uh the sentence is what I am Sunny now

uh here it is trying to find out the

relation I with M and sunny is trying to

find out the relation M with uh this I

and sunny it's time to find out a

relation this sunny uh and this m and

this I so it is trying to find out a

relation with each and every word so it

is doing the same thing inside the

multi-headed attention then you will

find out this feed forward Network

neural network actually and yes uh this

is what this is my encoder part as I

told you this is what this is my encoder

part now if you will look into the

decoder side so again we have a same

thing so here we have a outputed output

embedding means in uh uh like whatever

uh like a sequence uh in whatever like U

am format I want output so that is uh

this particular thing this output Ting

and then again we have a like

multi-headed attention and we are

passing this thing uh to the next one to

the next layer and again we have a feed

forward neural network over here on top

of this you will find out the soft Max

and finally we are getting a output

output probability so don't worry I will

try to discuss this Transformer

architecture mathematically in a

detailed way in some other video but as

of now I'm just giving you the GL

Glimpse because whatever llms we are

going to discuss

okay as a base architecture they are

using this

Transformer so guys until here

everything is fine everything is clear

please do let me know in the chat yes or

no yes you can uh let me know in the

chat uh then I will proceed with the pp

and all and uh we'll try to wrap up the

uh introduction of this llm and all and

in tomorrow's session we'll try to talk

about the open a and we'll discuss about

the open API and all and a different

different models of the

openi any doubt anything so if you have

any sort of a doubt please do let me

know guys please do let me know in the

chat uh I will try to clarify that uh

those doubt and

uh

so did you get a timeline timeline of

the llm I will come to the llm now the

specific word and

uh after deep learning an NLP what is

the topic uh for generative AI please

give up uh so after the Deep learning

and see after the Transformer actually

by using this particular Transformer

people has created a different different

llms and all large language model now I

will come to that by using the slide I

will try to show you that

I think this

is pry much clear now let me go back to

this uh uh notes and here you can see so

I started from the deep learning then

generative VI and all then you got to

know that where generative a lies then

Alm Gru different different mapping

encoder decoder attention and finally

attention all your need now let's try to

understand uh like rest of the thing by

using the slide so here guys uh one more

thing I think we were uh trying to

understand this particular part where

this generative AI exists and I hope you

got a clearcut idea now let me go back

to the uh let me like come to the next

slide so in this slide you can see uh

I'm talking about the generative versus

discriminative model so what is the

difference between this generative and

discriminative model so we are talking

about this discriminative model so

whatever you have learned so far in a

classical machine learning and deep

learning so uh let's say uh I'm I'm

talking about this uh any classification

based model let's say I'm talking about

this uh RNN so here actually see uh you

are training your model on a specific

data so this is your data this is your

input and here is your output what you

are doing guys tell me you are

performing a supervised learning you are

performing a supervised learning by

using this recurrent neural network

there is a classical model or we have

like other classical model and all you

can use any uh uh like a machine

learning based model as well like na

buers and uh different different

variants of the nap bias or maybe some

other model you can uh use that

particular model also uh so over here we

have a model and we are going to train

this model by using the supervis machine

learning there we are going to pass a

specific type of data to this particular

model and here we have a different

different output like a rock this music

is belong to the rock music classical

music or maybe ranting so here we are

passing this uh like music to my model

and finally it is going to predict

something like that this is a

descriptive model now if we are talking

about a generative model so this is a

little different compared to this

discriminative model how it is different

uh compared to this discriminative model

so here guys see we are training this

see first of all the if we are talking

about the generative model if we talking

about the generative model so the

training process is a little different

if we are talking about the large

language model if we are talking about

the llms so uh the proc of training this

llms is a little different compared to

this discriminative model now over here

we are talking this discri this

generative model basically so we are

passing the input to this generative

model and we are getting an output how

how so here basically we have a

different different step for Gen for

like training this generative models so

gain wise I already told you that what

is a like process if you want to like

train uh any gain model if we are

talking about llm large language model

so at the first first place there will

be unsupervised learning unsupervised

learning then at the second place we'll

be having a supervised fine-tuning and

at the third place uh basically we have

a reinforcement learning reinforcement

learning they have recently used inside

the chat uh in the GPD model itself uh

which we are using for the chat GPD but

before that whatever llm model they have

created they have created they have

trained on a large amount of data so for

that first they have performed the

unsupervised learning and then they have

performed the supervised fine tuning

so because of that that model were able

to understand each and every pattern

which was there inside the data and

because of that it was able to generate

the output so this generative model is

nothing in that basically we have a data

on top of that particular data we are

training a model and for that we have a

various step and uh basically then only

we are going to do a prediction so what

it is giving me as a prediction so

whatever input we are passing so that

input it is taking and finally it is

generating uh the output related to that

particular input means it is generating

a new

data getting my point I think this part

is clear to all of you how this

generative model is different from this

discriminative model discrimin model is

a classical model like supervised

learning uh we are performing The

supervis Learning now right so here we

are having the RNN and we are passing a

data and all and we are trying to train

it generative model various step we have

like for the training and all and it is

responsible for generating a new new

data that's it so I hope guys this uh

thing is clear to all of you now I have

kept couple of more slide regarding uh

this particular concept just try to note

down the uh the headings and all and try

to remind uh this particular thing this

discriminative versus generative model

and all now here uh the same thing

unsupervised supervised learning which

is related to this uh discr model got it

and here uh you can see uh this is the

generative like model so in the

generative model what we do first we

perform the unsupervised learning we are

doing a grouping and all and then we

discri we perform the supervised

finetuning supervised learning so that

is a like process for training a uh like

any sort of a llm model which comes

under inside the generative AI itself

and again wise I already talked about it

now here actually uh we're going to talk

about this uh llm so let me give you the

quick idea about this llm and all that

is what that is a large language model

so for that all Al I have created one

slide and there specifically I kept the

thing related to this uh llms only so

let me start from the very first slide

uh let me give you the overview and uh

from tomorrow uh actually in tomorrow

session I will give you the detail uh

like overview uh with respect to

different different models and all

whatever we have as of now just a quick

introduction now what is the llm so llm

is nothing it's a model it's a large

language model which is trained uh like

U it's a large deep uh it's a large

language model which has been trained or

a huge amount of data and it is behaving

like it is generating something right so

actually by using this uh llm we can

generate any uh like a sort of a data

like Text data or maybe image data and

that is a like uh that is a advantage or

that is a like uh uh one uh very uh like

a very famous uh thing regarding this

llm and all now if we are talking about

this why this is called llm why this is

called large language model so here guys

if we are talking about this large

language model so because of the size

and the complexity so here specifically

I have mentioned regarding this large

language model regarding this llm why

this is called this uh this large

language model so here because of the

size and because of the complexity of

the neural network uh neural network

neural network as well as the size of

the data set uh which has has been uh

which is trained on actually this is

trained on the huge amounts of data

because of that only actually it is

called a large language model so here uh

if we are talking about this uh L large

language model so uh actually before we

were not having the huge amount of data

so uh recently actually uh you know uh

this uh data generation and all uh Big

Data actually came into the picture and

this companies and all generated a huge

amount of data and this Google also

Google Facebook and the other companies

is having a huge amount of data so uh

they uh they are able to like find uh

means uh actually they have uh gathered

that particular data and on top of that

data they have uh like as I told you

they have performed the unsupervised

learning and all and they have

categorized a data and they have

provided to a different different model

which U like has been created like GPT B

and all and because of that uh like they

were able to predict the next next

sentence and that is a like a main thing

main advantage of this large language

model now over here you will find out so

in the next slide uh I have mentioned

that what is the what makes llm so

powerful so here by using one single

model by using one single llm actually

we can perform a different different

type of task like text generation

chatboard uh we can create a chatboard

also we can uh do the summarization

translation code Generation by using a

single LM we can do that particular

thing

now here uh if you will find out so

already I told you that what is the base

architecture of the llm so here this

Transformer is what it's a base

architecture behind this llm behind this

large language model and I have already

explained you the concept of the uh

Transformer that what we having inside

the Transformer now here guys uh this is

a few Milestone which we have in terms

of the llm like bird is there I think

you know about the bird if we are

talking about the uh we are talking

about the like a bad days right or old

days in 2018 19 or 20 when uh chat GPD

was not there just uh this uh GPT was

not there GPT 3.5 and all the recent

model which we are using inide of chat

GPT so there were few Milestone and we

were using this thing in our old days

like B was there GPT uh actually GPT is

having a different different variant it

is having a complete family GPD 1 2 3

and 3.5 and recently GPD 4 came into the

picture and other variants as well so

xlm is also there uh cross lingual

language model pre-training by uh this

particular guy now T5 was also there

this a text to text

transfer text to text transfer transform

Transformer and it was created by the

Google Now Megatron was also there so

Megatron actually it was created by the

Nvidia now M2M was there so it was the

part of the Facebook research so there

were many uh like uh there was the uh

like many model actually actually okay

and this was a milestone in uh this uh

in terms of this large language model

now over here guys see this bird GPT xlm

T5 they are using a base architecture as

a Transformer one only now if you will

see in the next slide so I have

categorize this thing so they are using

a base architecture as a Transformer one

only but in that you will find out some

of the model are using a encoder and

some of the model are using a decoder

and some of the model are using both

encoder and decoder now here I have

categorized this particular thing that

uh this is the model like B Roberta xlm

Albert Electra DTA so these are the

model they are just using the encoder

only and if we are talking about this

decoder uh if we are talking about the

GPT GPT uh 2 gpt3 GPT new or like the

entire family of the GPT so they are

using this decoder so we have a two

segment of the Transformer architecture

few of models they are using an encoder

side encoder part and few model

basically they are using a decoder and

uh here guys you will find out some

model which is which are using both

encoder as well as decoder so this T5

Bart M2 m00 Big B so these are the model

actually they are using both encoder and

decoder in the Transformer architecture

if you'll find out we have a two segment

so this is a this segment basically this

one is called an encoder segment and

this particular segment this is called a

decoder segment so here uh like I think

you got to know uh you got to know the

idea that uh this is what this is a like

uh transform this is the encoder segment

and this is what this is a decoder

segment and this model this T5 B M2M and

big but they are using both and we have

other models as well I just written this

uh couple of name over here now apart

from this you will find out some openi

based model open a based llm model so

GPD 4 is there GPD 3.5 is there GPD

based is there Delhi Biser iddings okay

so these are the different different

model which you will find out over the

open website itself and uh here uh

definitely GPT is uh one of the

prestigious model or this is one of the

very important model which uh people are

using uh nowadays for creating their

like applications and all and it is it

can perform any sort of a task related

to a generation okay now over here uh

this is the openi based model which I

have written now apart from this you

will find out other open source model so

this is the model from the openi side so

if you are going to hit this model so

definitely open is going to charge you

regarding the tokens regarding the uh

regarding like how many tokens and all

whatever you are using according to that

it is going to charge you but here we

have some couple couple of Open Source

model as well and I have written the

name like Bloom Lama 2 Palm Falcon Cloud

amp okay stable LM and so on we have a

various model various open source model

and uh yes but I I will show you that

how you can use this particular model if

you are going to create your application

so definitely I will let you know I will

show you that how you can utilize this

model as well I will show you the use of

the Falcon I will show you the use of

the Llama 2 if you don't want to use

this GPT uh GPT 3.5 GPT 3.5 turbo I will

show you the use of this llama and this

Falcon and some others open source model

as well I think you are getting my point

now here uh if we are talking about what

can llm be used for so if we are talking

about llm that what it can do so it can

uh we can use this llm for any sort of a

task like classification text generation

summarization chat board question

answering or maybe speech recognization

speeech identification spelling

character so this uh llm actually uh if

we are talking about this L LM so first

of all it's a model it's a large model U

it's a language anguage model and it's a

large model it's a large language model

and what is a like what we can do by

using this large language model we can

generate the data okay it can identify

the pattern of the data it is having

that cap cap uh that uh that much of

capacity so it can identify the pattern

from the data and by using those pattern

we are we can perform a various amount

of

task okay that's why this llm is too

much powerful

and here we can use this llm for any

sort of a task and yes uh we know about

this it and already I have uh like I

explain you this thing I hope this

introduction is clear to all of you now

coming to this uh prompt design so

prompt design and all uh definitely I

will talk about it uh once I will come

to this open a API there will try to hit

the uh different different models of the

open a and uh we have a different

different type of prompts so as as of

now you can think that the prompt is

nothing whatever input we are passing to

the model uh that is called input prompt

and whatever output we are getting from

the model itself that is called the

output prompt and here how cat GPD was

trained so generative of pre-training

supervise fine tuning and this

reinforcement learning there was three

step which I have mentioned so I will be

talking about this also and not in

today's session in the like next session

uh I'll be talking about this uh how CH

GPT and all it was stained uh okay now

what I can do so over here guys uh I

think uh we should uh conclude the

session so how was the session please uh

do let me know in the chat it was good

bad or what so did you uh understood

everything did you understand everything

whatever I have explained you uh

regarding this uh regarding this llm and

this generative AI the complete

introduction because uh I want that

before starting with any sort of a

practical the basics should be

clear everything did you

understand amazing

great to what is the

topic

yes fine uh if you have any doubt and

all so you can ask me I will try to

answer for that now before concluding

with uh like uh before concluding the

session let me show you few more things

over here so see uh here first of all

what you need to do uh first of all you

need to like uh go through with the open

a and you need to generate a open a API

and all so that uh basically don't worry

I will show you while I will be doing a

practical and all so you need to like at

least you need to create an account and

uh and you need to login it over here so

so once you will log in guys here you

will get two option first is chat GPT

and the second is API just go through

with this API and generate this API key

generate the API key from here don't

worry in the next session in the next

class again I will show you this thing

and here I see we have a different

different model so let me show you those

model and whatever open source model and

all is there so you will find out over

the hugging phase so let me show you the

hugging face models hugging face Hub and

uh here you will find out the model Hub

so guys uh here actually we have a model

Hub just a second yeah models now you

will find out a different different type

of model see these are the models which

is a open source and uh uh you'll find a

complete description let's say this uh

we are talking about this Orca 2 so this

updated 12 days ago and it's a recent uh

llm model uh which has been published uh

by the Microsoft now over here you can

see so it like you will find out the

complete description or complete detail

regarding this model and like uh how to

use it and uh each and everything

basically definitely we'll talk about it

now for what all task basically we can

use it okay so according to that also

you can uh like select the models so

just go through with the hugging phase

models and there you will find out many

uh like a different different models

okay and yes for sure openi is also

having uh different different llm model

so we'll talk about that we'll try to

understand the concept of this uh we'll

try to understand this uh assistant

actually and we'll try to talk we'll try

to understand the chat U actually so

what is this and how to use it how to

use this chat option and this assistant

option and here if you will go inside

the documentation so you will find out

of different different models over here

this GPT GPT 3.5 Del TTS whisper

embedding moderation GPT W gpt3 right

right different different models we have

and uh apart from that you can find out

the different different task according

to that also they have given you the

model so text generation so they have

given you the complete code and all so

just try to visit it just try to go

through it uh by yourself now uh we have

other uh platform as well so if you are

not going to use the uh maybe J uh if

you don't want to use this uh GPT and

all so here uh I can show you one more

uh like option so

AI 21 okay so AI 21 Labs AI 21 Labs so

this is the Recently I figured it out

actually this is the uh like alternative

of the GPT so we'll talk about this also

if you don't want to pay to this uh if

you don't want to pay for the GPT so you

can use this AI 21 lab uh and it will

give you the uh like a uh one model one

llm model so you can use it uh like

freely actually it gives you the $90

credit so so yes I will show you how you

can use this AI 21 lab uh so let me show

you the documentation and let me show

you the models as well so here you will

find out the uh model basically which is

there so Jurassic 2 is a model and it's

like a pretty amazing model and uh uh

yes definitely I'll be talking about it

and along with that uh the applications

of it which is very much required that

for what all task we can use it whether

if you are going to create a chat board

or maybe if you are going to do a

question answering or text generation or

like sentiment analysis for what type of

task we should use it and how to design

a prompt and all regarding the specific

task got it so we'll talk about this

also so uh yes many things is there and

definitely uh uh like from Tomorrow

onwards I'm going to start from the open

a lure and step by step I will come to

the uh different different models and

all so I hope guys this is uh clear

yes practical implementation will be

there don't

worry yes recording will be available

over the

YouTube yes all the uh all the topic

will be covered in the upcoming session

uh all the discussed topic and

all yes definitely this will be

available in the dashboard you can go

and check uh your dashboard this uh

video along with the video you will find

out the assignment you will find out the

quizzes and regarding the particular

topic fine I think uh we can conclude

the session

now is gener andm are also used in

computer vision based project so for

computer vision based project we have a

uh like others model we have a different

models uh because uh the task is

different over there so the task wise we

are talking about the computer vision

related task like object detection

object segmentation tracking OCR object

classification and for that we have a

different model and definitely we can

use a transfer learning of finding over

there now uh by using this L llm um like

this llm is for the like a different

task it is related to the language

related task it is not related to that

detection or a segmentation or tracking

it is not related to that particular

task it is related to the language

related task and uh here see let me show

you one uh one more paper one more

research paper so here I can show you

this uh ULM fit now see uh so just try

to go through with this particular paper

universal language model find tuning for

text classification now here in this

particular paper you will find out that

see uh this uh if you know the Deep

learning so in the Deep learning we have

uh two major concept so the one one

concept is uh called transfer learning

transfer learning the second concept is

called fine tuning F transfer learning

means what so you are transferring the

information from uh from one state to

another state okay or like you can I can

give you very simple example for that

let's say you know like how to how to

write the cycle so for you like for if

you know how to write the cycle

definitely you can use that information

and you can write the motorcycle also so

that is the same thing basically which

we uh do inside the transfer learning

let's say we have trained the model uh

like let's say we are talking about the

computer vision so inside that uh you'll

find out uh we have a various Tas like

detection classification tracking and

all so let's talk about the model let's

say YOLO so or we have other model also

like faster rcnn rcnn and all SSD SSD

and all like a different different model

related to a detection so the model

already has been trained on some sort of

a data some amount of data on some

Benchmark data so by using that

particular information we can uh like

perform the detection and all for our

specific task if we are not able to do

it then definitely I will fine-tune my

model but how we can use the same thing

in NLP because in NLP actually we have a

task uh the task is very specified we

have a specific task let's say we are

talking about um if we are talking about

a task let's say uh ner name entity

recognization or let's say we are

talking about the task let's say a

language gener language translation

language

translation language translation or

maybe sentiment analysis so these are

the specific task specific task ask

means regarding to the specific uh

regarding to the specific topic let's

say if I want to do a sentiment analysis

so not for the entire data whatever

there in the world for the specific uh

let's it for the Twitter data only means

whatever TW tweets and all we are

getting now if we are giving any other

data un any other like a task related

data so it won't be able to perform that

so actually in this particular paper you

will find out that how we can use this

uh language model language model

for uh like for the universal task and

there only this llm comes into a picture

L this llm actually it came from the

language model itself okay because we

are training this uh language model on a

huge amount of data that is why it is

called llm large language model because

we have we have trained this data on a

huge amount of we have trained this

model on a huge amount of data got it so

here in this particular paper they have

like shown you that how to use this

transfer learning because uh in before

2018 uh actually we were using this

transfer learning in the computer vision

only in the uh like in a different

different tasks of the computer like

object detection or segmentation you

will find out the image data so on top

of that data we have trade like a Ben

model and directly like like vgg rset

and all and directly we are using those

particular model for our like a other

task so here if we are talking about the

NLP so we are not able to do it before

this Transformer and all so actually see

we got a Transformer we got this

particular concept like how we can use

this transfer learning and all in the

like NLP field so this two concept came

together transfer learning and the

architecture like Transformer self

attention and all and from there itself

this uh llm came into the picture llm

means large language model which has

been train on a huge amount of data

which is able to perform the transfer

learning and we can do it uh we can find

T it also and the main uh like uh

the main cap or the main uh role of the

llm is what it is able to generate a

text text generation got it so fine I

think we are done with the session now

so rest of the thing we'll try to

discuss in the uh tomorrow session and

we'll try to more focus on the Practical

side so all the recordings and all it

will be updated on a dashboard and uh

yeah that is it yes so I think uh we can

start with the session now so yeah uh

welcome again again uh so you all are

welcome in this uh Community session

generative AI Community session uh

yesterday we have started this uh

generative AI Community session where we

have discussed about the generative AI

so there I have given you the

introduction related to the generative

Ai and large language models and here

you can find out the video so this is

the dashboard uh it's a free dashboard

actually uh which we have created for

all of you the same video actually it is

available over the Inon uh YouTube

channel as well if you we will go in a

live section so this uh same video you

can find it out over there as well now

uh let me show you uh that particular

video so here is my YouTube now let me

search over here I neon and here guys uh

go inside this uh live section and this

is the video so the same video same

lecture you will be able to find out

inside the live section apart from this

uh the same uh thing basically we are

uploading or the in neuron dashboard and

here along with the video you will find

out the resources so all the resources

basically whatever I'm using throughout

the session uh so whatever notes and all

which I'm writing and whatever PPS and

all or whatever code file I'm using

throughout the session you will find out

all the resources over here got it guys

yes or no so do you have this dashboard

do you have this dashboard tell me I

think uh many people are androll

yesterday uh for this free community

session and yes all the videos and all

basically we are going to upload over

here not even video and resources along

with the video and resources you will

find out the uh live uh you will find

out the quizzes and assignment as well

so already uh I have prepared the

quizzes and assignment so soon it will

be uploaded over here so in this

particular video you will find out one

more section so the section will be

quizzes and assignment so there you will

find out a uh like uh a video related or

a topic related quizzes and assignment

got it yes or no so please uh give me a

quick confirmation in the chat if this

uh dashboard related thing and this uh

video related thing is clear to all of

you I'm waiting for your reply in the

chat and if you have any sort of a doubt

then you can ask me in the like chat

section as well and don't worry my team

will give you the link of the dashboard

so if you haven't enrolled so far so uh

by using that particular link definitely

you can enroll it you can enroll U

inside the

dashboard great all clear all clear

great fine so here I got a confirmation

now uh so let's start with the session

let's start with the uh second day so in

the first day actually so what I

discussed I discussed about the

generative AI so where I have like told

you that what is a generative AI so this

is the slide basically which I was using

and here actually this was the agenda

the complete agenda which I going to

discuss this uh throughout this

committee session and uh today is the

second day where I will start from this

open AI so in the previous session I was

talking about this generative Ai and I

discussed each and everything related to

this generative a and I hope you got a

clear-cut idea that what is a generative

Ai and in the generative AI actually

what all things comes into the picture

where llms lies so if we are talking

about this large language model so

regarding the large language model also

I have clarify each and everything I

have given you the complete timeline of

this large language model where I have

discussed about the uh the complete

history of the large language model from

the RNN so first I have started from the

RNN then I came to the lstm then uh I

discussed about the uh different

different sequence to sequence mapping I

talked about the encoder and decoder and

after that I have explained you the me

uh the concept of the attention and then

I have discussed about the attention is

all your need uh the Transformer

architecture and I told you that

whatever llms which you are seeing

nowadays so those uh all the llms are

using Transformer as a base architecture

so I have explained you the

the like whatever thing was there inside

the Transformer architecture whatever

component whatever segment was there

each and everything I have discussed

over there and apart from this

generative AI I have talked about this

llm also so there I have talked about

that what is a llm why it is called

large language model and uh why it is so

powerful because this one uh because

this one llm is able to perform lots of

like task lots of uh one basically llm

we can use for the different different

type of application so here uh I have

written the couple of name like text

generation summarizer translation or

code generation and so on we all know

about the chat GPT uh chat GPT is are

application and uh chat GPT is using

gpt3 gpt3 is a a base model so GPD 3.5

actually it's a base model so how it is

how much it is powerful we all know

about it and that is a example of the

large language model which is capable to

do so many things why because uh it is

having a power so so that actually it

can generate a it can generate a data

based on a previous data it can

understand the pattern and because of

that only we are able to trans we are

able to use this Transformer as a or

whatever like Transformer based model we

have we are able to use those transforma

based model as a transfer learning I

have explained you the concept of the

transfer learning and the fine tuning as

well so here I was talking about this

llm and then I talked about the few

milestone in a large language model so

here I have written couple of name bird

GPT xlm T5 Megatron M2M so these are the

uh like a few milestone in a large

language model now this model has been

trained on a huge amount of data now

specifically we are talking about GPT so

in a GPT Family itself you will find out

of various model I will talk about it it

I will come to the open Ai and each and

everything I will keep in front of you

only and uh I will uh I will show you

that how much it is powerful so we are

talking about GPT so it's like really

powerful and it it has been trained on a

like huge amount of data and it is

having a billions of parameter so here

is few Milestone so in our back days in

our history basically we are using this

particular models now in a recent day we

got so many uh architectures so many uh

open source models and so I will talk

about uh regarding those model as well

so here in the next slide I have shown

you so what all encoder based

architecture we have what all decoder

based architecture we have if we are

talking about anod and decoder right so

in which architecture you will find out

both encoder and decoder if we are

talking about B xlm Electra DTA so these

all are these all the architecture

actually it is based on an encoder we're

talking about this GPT GPT family so

it's a based on a decoder itself and the

idea has been taken from the Transformer

itself now here you can see this T5 Bart

M2M big but so these are the model which

are which is using this encoder and

decoder both got it so now here uh then

I talked about the openi based llm model

so here uh the very first thing comes

into the picture that is a GPD itself

GPT 3.5 which is a like base model

behind this chat GP chat GPT just a

application it's not a model now here

you will find out this Delhi whisper DCI

there are many model I will be coming to

that particular model and I will show

you how you can get all the model from

the opena itself and uh yes we'll try to

use those model for our task for our

like a uh for the for our like a like a

requirements and all definitely will try

to use this particular model like GPT

GPT 3.5 I will show you how you can use

GP 3.5 turbo viso da in or other model

as well like aming and moderation so

apart from that this apart from this uh

like uh Milestone whatever Milestone I

shown you and this openi based model

here you will find out some other op

Source model like Bloom llama 2 Palm is

a model it's a very famous model from

the Google side nowadays like most of

the people are using this pal Falcon is

a model cloud is there MP 30 uh MP is

there uh this B actually it is showing a

parameter right and here we have a

stable Im so a stable LM so there are

like so many open source model so I will

come to that also and I will show you

how you can utilize those particular

model and apart from that I have like

kept some more slight over here inside

this particular PP so you can go through

with that and you can understand some

other uh like concept like how this chat

GPD has been trained and all so I hope

guys still here everything is fine

everything is clear

now we can move to the Practical part so

please do let me know in the chat if

everything is clear so far in terms of

theory guys I'm waiting for your

reply uh just a wait so let me give you

the link of the website and

uh