Artificial Neural network is a computing system
designed to replicate the way humans on lies
in war it forms the base off all
artificial intelligence concepts that it's like you have
come from this your guardian on unit moves
Now before we go ahead recession I'd like
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of the details in the description below Now
let's have a quick glance of the agenda
so we'll start off typical gentle introduction to
artificial intelligence and deep learning Then we'll understand
whereby large failure let works and how it
inspired us to come up with the concept
of artificial neural net books Going head will
understand the implementation of artificial neural networks pulling
which will called pensively understand what back propagation
and then finally will have rhythm over will
be implementing this artificial neural networks with our
language Let's start organization in this video even
see what is artificial intelligence What does it
mean how it has evolved over a period
of time and what does it mean to
you and me And what does it hold
for us in the future Artificial intelligence has
been getting a huge moment in the recent
world and it's making a tremendous impact you
know career Oh comes But before we delve
into that let's understand what this artificial intelligence
So what is it Let me ask you
a question Look at the photo Bo And
can you guess who that famous personal days
I think most of you guys would have
guessed that it's Albert Einstein but it's not
an easy problem Think about how your brain
process that information and how did they recognize
it It might have noticed the facial features
It might have noticed the curly hair It
might have noticed the distance between the ice
It might have noticed another 1,000,000 features to
come to the conclusion that it is Albert
instincts So can you make a mission to
think the same way which you think that's
the process off making a mission to think
like how a human dust is what is
north However human's ability to think and make
a rational decision in a critical situation is
unfair Any that science is not able to
correct it So what does missions have for
them How is that speedy And how is
it doing good What do they have is
a competition and this is gold Massively In
the recent years let me ask you what
is the square root off 200 You'll not
be able to tell me a second but
where is the computers can But if you
were to ask a computer to take a
bottle of water from your kitchen will be
ableto well The computer needs to know what
is immortal What does water me and out
of a mezuzah Kitchen fans the humans that
don't have any problem in that So making
a computer from done about its surroundings adapt
and then group is also hear The term
artificial intelligence has been known since a long
day It was first coined in 1956 by
Mr John McCarty at the dartboard Conference so
the confidence was hit on the garnish A
lot of attention from a lot of players
we're creating this passions are creating the smart
questions required a lot of computation power on
hardware just apparently no there at that point
of time Well the tipping point came in
the late nineties where IBM's Deep Blue stunned
the entire world by defeating the world champion
at Chester So not a fully factually We
see fragments off the implementation in our daily
day to day life Think off the next
time in your type in Google search How
does it come up with the fact What
are you going to take next Google has
tons of data based upon what have your
type so far on its L gold ums
are running out the back to find out
what I want right next based upon what
everybody Netflix predicts what movies you want to
wash next based upon what movies you're washing
The bust that is Facebook detects all your
friends in the same photo which have taken
on uploaded based upon the information it has
gathered in the past About your friends in
your own profile is usually divided into two
broad categories Freshen learning and deep learning Mission
Learning is the use of statistical methods to
understand and analyze information from a given data
It is deep learning a broader area mission
learning or abroad A subset of mission only
mostly bills with your notebooks computations are growing
exponentially Year on year the scope afia has
become bust and is being used in almost
all the door It's in all the verticals
and across all the industries Beat idea retail
calico B A for safe travel e commerce
Any of this industries the eye And Emily
has a future in all the facets off
these industries off This video was helpful to
you guys If you like this video like
share and subscript all channel and stay connected
bitters for future it's So what neural networks
are we'll start off with seeing briefly what
a biological neural net book is Um we
won't go into matter off The details will
just do we know of you because it
helps to understand what an artificially order network
does Um then we'll see the overview of
artificial neural network and a small example Ah
pretty small example on Ah what happened and
how in your network loans We'll see how
we implement Billy and and and different steps
that are a part off implementing artificial neural
network Ah one off them being back propagation
than now the weights up station and then
the error function Then we'll also see the
somebody off What The overall learning algorithm that
the neural network for lows on dhe Then
we'll see how implemented over the network and
are using the same using that same either
status that that we used to do so
that we only know what There's Theus sense
of fuse in your love network in art
and were able to compare the output off
what we did with provisional Carl rhythms and
what the neural network gives us Oh okay
So what by logical neural networks you would've
seen already how They're the basic building blocks
of the um certain What's that The central
knows systems or these other diagrams which may
be repetitive for you But then they help
us understand what our biological noodle network does
not going very deep in tow By Lord
T Um So it's something that you see
in the yellow is a new Ron Okay
which is similar to a neuron in the
artificial neural network Each contains some data about
the arm input data variables Okay So like
the neurons are interconnected at the point called
synapses So in the diagram above um you
see lead soak away there is a synapse
So neurons are interconnected at the wind closing
up this and this is similar to the
artificial neural network because of the different in
what way Variables that you take Um that
information is passed to the hidden layer And
how that information is passed to the hidden
layers is through all point with a similar
door The synapse in the biological neural network
Um two There are three parts that are
typically present didn't by law to communal network
Ah one is the dendrite which is the
law Um we can stay like terminals off
a noodle network with the responsible to receive
signals from the surrounding neurons than ever Accent
with They're nothing like a but so are
comparing it to the artificial neural network like
there is a part when you go from
the input layer to the hidden layer there's
a part in between our which carries the
information's on the accidents similar to that then
it's an obsolete as an abscess are responsible
for receiving the signals and learning from the
past activities How we correlated to the artificial
neural network It's um because if you if
you know off the concept of back propagation
Even the artificial neural network loans from it
past activities and to ableto kind off relate
the biological neural network Pretty artificial neural network
to eat off your onshore act as a
neuron in the artificial neural network The accident
as a part where the information is traversed
from one letter off new network to the
another layer of neural network and that then
right onto Napster's I usedto send and receive
information between one neuron to the other And
then they also enable the neural network to
learn someday passed after with ease And that's
how the AL garden improves itself Okay so
uh this is essential to I understand Only
because the artificial neuron our network gets its
name from the biological Your electrical Okay Um
so this is again um smiled I gave
them to just show you how the information
is passed out with a biological unit Work
on a similar kind of thing happened with
the arm Artificial unit workers Well capital What
happens is sell whenever assorted information is um
coming toe a neuron when a certain information
is present in the new Ron Um So
there's this thing called tray shoulder which would
also see with the artificial you know left
book So once that threshold has reached one
that special has being achieved The neural network
transmits a signal through the ag zone And
the similar kind of thing happened with the
artificial neural network is well whenever the threshold
is reached our activation function trekkers and then
an information is passed from the input Um
let's dig up a clear So we'll see
how each of these steps in the biological
neural network helps us also understand what this
President Lee artificial neural network Okay so this
is Ah how ah basic off how in
artifice in your little book works So the
input that you see here are nothing but
importantly how variables over the data is that
they're working with Then there are certain weights
which are applied ahh to eat of the
important media boots Um until the way it's
not apply that nothing but the help the
noodle network tell What is the importance of
each off the way vehicles So this is
also what we saw with the previous Alcala
TEMs be alone So each way be able
that is present in our row input data
set does not have equal importance Get each
input variable does not have equal in port
Instead I'll be rebuilt with More important they're
very bitter Yes we're important How that has
decided is through these weights So whichever variable
is given a higher rate is comparatively more
important than a variable Gaza Yes sir Wait
So this is how the weights are amused
in the northern network So the set of
inputs which are nothing but um the variables
from the leaders at that thought off us
neurons This is how we correlate an artificial
neural network widow biological neural network s So
then there read according to the weights that
are applied what I just told And finally
this is Oh we do kind of a
some product off the input variables with the
weights Some product means to say X one
in tow Doubly one place extra int o
w topless extra into w tree in so
on Okay so the function that you see
here are the summation function of the Sigma
function Here The skin also be Caldas It
can't work function So this actually performs the
some product Settle some of the weight on
the input that it receives from each off
the neuron The next step is to go
to the activation function which will decide whether
or not you're on mill fires the similar
to the biological neural network wearing with the
threshold we decide whether or not arm the
information should be sent to the next flesh
Okay So if if the threshold also whatever
some you get using the transfer function are
you compared it to the threshold If it
exceeds the threshold the activation function decides to
fire when it fires the information is passed
from the inn What led to D out
clear like similar to what happened in the
biological neural network When the activation function fires
information is passed from one led to the
other Okay so like I told earlier also
the wait can be used to amplify our
d amplify the original input signal meaning to
say they are They're used to determine how
each variable will be used What is the
importance that they be given to eat off
the variables Okay Took on today the simple
example So this example it's got nothing to
do with the neural networks actually um consider
that you have to say to input variables
X when the next to um So these
are two input variables in a data set
One off them having away our value off
0.6 and one off them Having overly off
one Um on the weights Say that applied
to both of these input variables at 0.5
and zero point it again you're also being
given out the shoulder off want So what
happens in simple terms in the neural network
is the first step off A chance will
function what we saw in the previous light
off Nothing but the sound product will happen
here So 0.6 in 20.5 plus one into
0.8 So that gives you 1.1 So this
is similar to the R summation function that
we had seen here Um next we compare
this value that we've caught with threshold to
say our value Oh that we've got is
1.1 But the pressure lives 1.0 So we
see that output this more than the threshold
that was allowed So since the output this
greater than the threshold So the neuron is
activated and it fires Okay so this is
a very simple example This is not what
happened Actually happens The neural network because they're
in a bed innovative off the other function
And all that is calculated toe edit function
is nothing but the actual output Ah a
difference between the actual output and the desired
output So they're not dead innovative off that
is calculated and passed back to the ah
neural network But I put this example only
to show you how the trash world works
So when whenever the output exceeds the threshold
the neural network will know that it needs
to fire Um the signal What is meant
by fighting the signal is nothing but up
back propagation Meaning to say that whenever and
now put increase the threshold Um and the
algorithm will I'm sorry Whenever the Albert and
creators threshold a bad propagation will occur and
a reiteration reluctant annuity operation within a neural
network will occur And the output other l
gotta temple tried Oh improved the output Okay
Now we'll see how we are Implement the
artificial neural network So this is a simple
diagram to show how a neural network works
So there are for example three input variables
to output variables here So they can be
any number of hidden layers that are present
in the neural network And the hidden lives
actually perform whatever Um plea and garden needs
to be applied Is stunned by the hidden
layers only Ah there are different ways on
how we decide Ah how many hidden layers
are to be present and we'll see that
when we work with are So what happens
here is that the input Claire will take
the input from the input variables So say
you have three variables here so each input
eat off the neuron that you see in
the input layer will have data from one
variable each The input layer will take input
Junkie input variables The hidden layer will help
The import led to move to the output
clear canto The input layer will pass the
information to the hidden layer with the weights
present And then they hidden layer will move
that data to be up there But the
hidden letters essentially oh big black box lay
like I told you earlier you will never
be able to understand what is happening with
the hidden yet on the output layer will
stroll you the final output So this is
a Naderi toe A more detailed on diagram
off the off a diagram So what I'm
trying to show here us that there could
be any number of notes that once can
be present at every layer And then there
are who waits that are applied to the
Excuse me Okay so then the weights that
are applied to the input that take them
to be hidden layers if you see in
the diagram vill Oh there are three in
boats 123 And then there are some weights
will turn up blade do the each off
the in boots when it is being taken
to the hidden layer So each input variables
with some way the plight will goto eat
neuron in the hidden here So if there
are three input variables on there are Rome
three hidden layers So eat off the importation
The input arm I want Will goto eat
off the neuron Really hidden Blair on a
different weight will be applied when it goes
through each often you're on in the hidden
there and there is another category off wait
So one is the way it's not applied
to the input variables There's another category your
fate which we call Astor bias values Tobias
values are a can use to train the
neural network So this this can be compared
to if you remember with on regression Also
we used to have a similar things where
now we'll have some weights that are assigned
to eat off the baby a ball so
that woody coefficients back in our regression when
ah you used to get immigration something like
Why equals to arm a plus b one
x one plus b two extra glitz be
three extra and so on So then they
were coefficients are in regression out of weights
returned neural network But there was also an
intercept which was the unexplained our variance or
unexplained output that the intercept used to capture
so similar happens here with the hidden bias
So that is some information that the input
input variables along with the weight cannot cap
after that was captured by the hidden bias
So but eat off Um the letter that
you have apart from the input layer there'll
be a bias entered like if you see
in the dive from here with the second
layer or that is the hidden layer present
in the between There's a hidden bias that
is added on with each hidden land that
you have Like here we have just Hinton
lead but each hidden layer that this present
they'll be open hidden bias for each off
the layers on Finally they're old They will
also be an output bias which is present
for the output layer Nothing but the intercept
What We used to have it The person
in question Okay that was the basic off
What an artificial neural network does will goto
back propagation Now to see how the neural
network loans are camped on the basis of
a neural network is a powerful learning Morgan
is um um although it is a powerful
learning McCann is um but you will never
be able to understand what that landing mechanism
is But that being said though you relent
work has a power for learning McCann is
up on It can learn any function given
it has enough hidden units So eat off
the hidden units that are present enable it
doe loan tow the input data set Um
and how it loans us by the mechanism
that we call us back proper location Okay
so like I told you earlier also when
on in Portis fed to the neural network
that is when the input variables are fed
into the noodle network Um the input will
some weights will be applied to the important
actress Oh coming Some bias will be applied
to the in addition to the input that
is coming And finally all that data will
go to the hidden layer Same process will
be repeated on the data will move to
the output layer When you get the date
I'd be out clear The difference between what
the actual output or the desired output waas
and the output that you are getting some
era will be calculated based on the difference
Whatever is the error will be fed back
to the are forced layer on the whole
process Repeats Yeah So these editors off it
back to the neural network and then these
weights to the question that they were asking
the weights are then changed in order to
try to reduce the errors and give the
correct output to the first situation Random rates
will be assigned but with registration that hap
opens the weights are changed Um using the
errors that we have caught in the previous
letter This is the diagram that explains back
propagation So there are some input variables Then
there is a hidden layer So say there
are oh tree input variables here Then there
are no hidden um neurons with wth e
headed layer So eat off the input variables
is being all fed to the hidden layer
And then there's also a bias which is
being applied And then from the hidden lead
we're going to the output layer on the
output layer The other calculation happens which is
nothing but the difference between the actual value
and the calculated value Whatever is theater calculation
is bad Propagate that and then the weights
are want to fight This happens each time
the algorithm chants and that Saudi wait are
improved And that Saudi unguarded um notes Okay
Okay so then once um you're done with
back propagation is all you did with back
propagation wants to get the error and pass
it back to the old input layer so
that the weights are modified Now how the
weights and quantify it is Um after you've
done the back propagation you need to update
the weeds Now if you remember with the
previous slide that we've seen here there are
rates apply to the hidden layer and then
they have big It's applied to the output
layer as well The both of thes weights
are different Okay so um don't get confused
But what Ah the equation here is But
this is just mento show that there are
no different weights that are applied off the
boat off the layers So w idea that
you see here is the week that was
um passed to the ray labels from the
import lair on W J K is tthe
e awaits that applied to the neurons from
the hidden layer comes of both off them
We'll launch from the errors that they're doing
on both off the weights will be updated
Don't go very deep into what the mathematical
terms here are But this is just meant
to tell you that both off the wait
will be more the fight based on the
error that we're seeing from the previous generation
off the car There's another thing which you
would be CEO to seeing here which is
the Alfa Value So Alfa values nothing but
the learning rate known ingrate means to say
at what did it do You want the
neural network toe loans and this is nothing
but the learning speed Okay if you make
the learning speed too quickly are you make
the Alfa value You don't really specify these
values This is what happens the background But
if the learnings bead is all very quickly
the um sorry the learning speed is very
faster now for Bradley was closer to one
theory garden would not be able to learn
properly This is not a thing like nothing
like um trying to say that you're trying
to learn something very quickly So you destroy
I know what you don't understand the sense
of what was there in the data And
if if you make the learning rate very
small you don't allow the en garde tomato
grow You don't allow the unguarded Tinto learn
on the go on optimal learning rate ISS
chosen But you don't choose does it Thean
garden tools is itself but an optimal our
learning latest chosen for each situation that happened
with the neural network So when back propagation
will locker some errors would have occurred And
then a back propagation with lacquer that it'll
will be passed back to the ohm and
God autumn the weights that are being passed
that are being applied to the input data
variables We modified the weights that are being
applied to the hidden life It all to
be modified and finally or you won't put
within produced And this process repeats across multiple
it rations And each time the weights will
be updated him Um then we come to
the error function Every function is nothing but
again the difference between the actual value and
the desired values Your um so although this
is a repetition but um the input lady
able will be passed to a system so
adaptive system here is nothing but the job
hadn't left And then you get up output
by you Compare that output to the desired
output Whatever is Theo error is fast back
to the adaptive system Nothing but the hidden
left Then the waiter want a fight Um
said one thing to keep in mind this
that the other should satisfy some particular properties
off This was this was present in the
videos as well that we saw on the
first thing being back The error should be
um that I ve been meaning to say
that it should be possible to do what
that awaited there But there was nothing but
the by the ex What we used to
do back in 11 12 mathematics it should
be possible to do a derivative Why um
To answer to one of the questions earlier
on the threshold that we calculate the threshold
that we specify is a declarative off the
road Well capital if if it does not
remember So the threshold tingle not books that
special that we're specifying that we're telling the
algorithm is nothing but that of a tough
off the error that we're getting and each
layer Okay um as this is very obvious
Although but as the output moves towards the
desired value the error should obviously move toe
zero enter should be negative again for the
reason that we should be able to do
with that everything off it um and then
are we needto choose on the error The
other value sorry We're not using the air
around Iraq actually But then ah when there
is a new low value for the error
or they're too high values for the error
that actually are impact how the neural network
works If the value is too high the
accuracy of neural network will be bad And
then it is the lot of it rations
to our be able to learn on if
the error value is too small Sorry that
I have perfect Ah opposite So if theater
body was high then wth e you're a
nettle will take a long time too long
I understand that after the session Okay so
we'll finally see an overview off What happened
with the learning and guarded items Um in
the learning and guard it immobile So the
back propagation actually allows the neural network to
get trained similar to how we train all
the machine learning all the data mining models
Similar way the back propagation allows the neural
network toe loan Um this happens in multi
penetrations What I've been calling so far Ah
these situations in terms of Euronext booker also
call s it box So it goes through
several e box before the network has sufficiently
loaned to handle all the data that has
provided this is again a different diagrammed shown
in a different way to show how back
propagation works back to fit That will happen
This is that it happened for eat off
The interational eats off the box are the
input data will be fed Thea output data
will be compared against the desired value and
then it will be whatever the area will
be will be back Propagated Amusing The other
toa data Um Now then well this stop
meaning to say that each time when um
particular era is calculated based on ah what
was the output boss is what was he
desired Output And then a back propagation occurs
So this process keeps on repeating So like
we used to have Ah Bruning step on
stopping cripe area Ah for the decision trees
that we saw last week Similarly there should
be a stopping criteria Sure A swell sir
Multiple stopping right areas can be useful Neural
networks one off them being our desired mean
square error and one of them being in
love Steve Box desired means wherever tells what
is tthe e enter that we can allow
the data Otto has Ah what is Thea
flexibility that we're giving the data toe have
on elapsed boxes Nothing But what is the
number of operations that we want So a
neural network cannot run indefinitely through 10,000 and
one large creations Dismayed Ease with the data
that you have but it cannot run indefinitely
So we need to specify the number of
iterations of it's a neural network should learn
So both of these are I used us
stopping criterias for the urine Okay um again
I'm using the idea state That's it that
we had um scene with the previous home
and gardens is Mel um to give you
a quick revision about the iris data set
Waas So the status that has won 50
entries on which there are four input variables
with our polenta were turned better lengthen bit
And finally there are three species 50 off
beat species and the aim off this in
garden is toe Try to predict what important
what is the species of a particle of
flour given its attributes off Zeppelin Patty And
so over the last session we try to
protect the species using card um using no
random forest We also saw the same example
but plastering as well when we were tryingto
use disciplined with Stan Bedlington with Joe I
don't plaster these so class together so we'll
use that same way Does that Um I'm
repeating the steps what we did earlier off
generating around them number shuffling the details of
this again is oh um optional step But
I normally do it so that there's no
bias ness which is present in the data
One important step over it took you know
down here is the line number 12 that
I have with our cord If you remember
with um noodle network this was being done
in the example that we didn't understand all
Toby was full in the example that you
don't but ah you many project So it's
that is any really ability is off Categorical
were able something like um the species that
you have here when businesses where it said
oh so farcical And Virginia Command you have
three species You cannot have the data in
this format when you work with the other
networks so you need to have three columns
be done informs off one and zero So
either you can do that with multiple commands
Either you can create um three new input
variables and then pass Wanna or an zero
here but a simple step Awesome Bill command
is the class start I I nd command
which you can heels in one goto create
all the three variables Let me show that
to you how it happens If you see
here with a single command I'm able to
say cricket the initial species that I had
If you see the species I had here
was um in the form of settles over
Jenna gone warsi color and then have split
that the fine form off zero and one
with one single How canto Um what was
done earlier Waas we usedto create We used
to do that Oh for eat of two
variables one by one But he was in
this command You can now very simply split
the baby about in the required farmer Okay
And as a short cut what have also
donors have done the scaling of the same
command here So what does entreat it was
from the nation either Sunday because it that
I heard I scaled the 1st 4 berry
a bill to the 1st 4 variables off
separate length with Bette London but was killed
And then this week um the output variable
which was one was splitting form off 011
toe be ableto fit the neural network So
then I am um simply dividing my data
set indoor training and testing that does it
Ah so the Fleer 70 30 but not
exactly are the 1st 100 And passing to
my 1st 100 rose and passing to my
training data set andare next 50 to my
testing data set And finally I'm creating a
new internet It works well See Sure What
is deep Fatemi does that we passed to
the your network Um the first thing is
the formula that we need to give here
So the farm last year tells me that
these three which now become my um output
variables sees that those of our sickle and
birds in the government's now become my output
variables are a function off four independent artie
input variables So this is the formula that
I've specified data is the training data set
hidden specifies the number of hidden layers that
you want Also a tumble to tools Three
hidden layers is all under route off the
number off input variables that you have So
if you have I had four input variables
here so I chose you Okay And um
since you saw you can have multiple hidden
layers here So say if you'll have um
Oh maybe like hell I had in what
Lady of Birds And hey didn't let so
since I had four input variables I did
a square root off Um this do tools
that tire if half door hidden hit unequal
still toe here Well second Okay And um
sorry to hear means I'm rounding it off
to get a toe So that will mean
that I have one hidden layer with two
neurons Say you have um maybe when be
input variables off in place of four So
you do a square root off 20 It
should be 4.47 or new Round it off
to five Then you do a square root
off five And that could be 2.3 So
you can rounded off to Probably toe are
three So what I'm trying to tell you
is that when there are four input variables
I'll have just one hidden layer How It
will have to note when you have say
20 input variables you'll have two hidden layers
The first off it will have five hidden
neurons to say enough it will have um
don't neurons Okay Making sense Oh like if
I have 100 input variables I have ah
first hidden layer which will have any neurons
Then I'll have second hidden layer which will
have three neurons then Ah yeah tow that
you'd be here Um see I have 500
input data variables So I have first hidden
layer which will have um say 22 hidden
Sorry I'll have first hidden layer which will
have 22 neurons Then I'll have um second
hidden lead rich and have 4.7 or you
can round it off to five neurons Then
again I'll have third hidden layer which will
have don't neuron So this is how you
decide the number of hidden layers that you
have the data and number off neurons that
it be present in each hidden Let me
let me go Enough idiots that under this
again I'll get so in we had 1/2
note and then the hidden Nair's So when
I had for input variables I do a
square root off four That will be so
So I have one input layer which has
the sirah Buta first hidden lair with I
do neutrons Okay so you're kind off Keep
doing the square Wrote multiple times So I'm
doing a square out here the first time
Second time when I do a square root
off So I come to approximately one So
then I stop Okay Meaning to say when
I have four input variables I get it
too So that will be first hid in
leather with don't neurons say if I have
n input variables I'll go a square root
which is approximately equal to three So I
will have the first hidden layer with three
neurons and then I'll do a square root
off three which is 1.7 and you can
round it off Duke So So I have
a second hid then layered with your neurons
Okay Now we've now if you do a
square but you keep on doing the square
root it creatively Eso Now if you do
a square What You'll get something like 1.1
point four somethings are there You stop So
with 10 input variables you'll have two hidden
layers So with the hidden layer perimeter that
you're specifying here I didn't know here because
I had just one hidden there So you
need to give hidden equals toe So here
with the 10 input variables you need to
give her the unequal still see off three
comma tow three means first with three neurons
Second with two neurons Okay Like a bigger
example If we take I have 540 variables
so I do a square root off 500
lie could be say 22.36 and I'm rounding
it off to 20 tow so I'll have
first hidden lead with 22 Newman's Then I
am doing a square root off Hey Needles
That gives me 4.69 and if I rounded
off to five so I'll have a second
hidden layer with five new Ron's Then I
do a square root off five square root
off five which gives me 2.23 So I
round it off to know for example So
then I'll get 1/3 Hey didn't lead with
um doing your guns And then when I'm
doing this with our I give hidden equal
store when Tito comma five comma So this
means three layers first having 20 Don't You're
on second having five neurons thought having Don't
You're nuts Okay um till next is um
the threshold So their shoulders nothing but their
innovative off the error that will be calculated
that each step So we can also see
that from the help in our age See
the special little specialise No medical value specifying
the threshold for the partial derivative of the
error function as a stopping criteria So at
each step some era will be calculated as
a difference off the actual value and the
desired value And then your calculator derivative off
it So say the error in the first
step is so for example so you calculated
a derivative Elphick then bye propagation with locker
then a reiteration with Legrand this process keeps
repeating so this threshold equals to 0.1 sees
that keep repeating until you receive or derivative
of the aerial function which is 0.1 Okay
So in the first on the force situation
may be on the air Live off derivative
Off the error function will be safe for
example one Then you back propagate Reiteration occurs
then the derivative off the edit function maybe
comes down to 0.9 Then you repeat the
back propagation occurs then Ah maybe the that
give off the other function comes down to
be 0.6 10 0 point does you don't
find one and they left Comes down to
0.1 You need to keep repeating the card
Okay Um the next step is So this
is um one stopping great baby A They're
shoulders one stopping criteria Step Max tells you
what is the number of it rations You
want it It's possible that um hey went
with 10,000 integrations or 10,000 cycles The threshold
doesn't read 0.1 doesn't mean that you'll keep
planning this algorithm in finite times Step Max
is another stopping right area Which tells us
what is the number of iterations that we
can allow one Your network tohave The step
Mexico's to 22 2000 tells us that we
need to um we can run this and
got it on a maximum of 2000 times
Okay um Selenia output equals two falls is
nothing but um telling whether or not you
want the activation function to work So ah
tau revisit activation function was nothing but allowing
a neural network to fire a trigger Firing
a trigger means allowing in your network to
backfire So very bad Proper get So when
When new specify Lee near output equals two
falls That means that you're allowing the activation
function Okay S o with the lectures There
were different activation functions that would also given
But there's not much difference practically speaking with
ah the different activation functions And even if
you don't specify it explicitly with the commands
you know you're not missing out anything This
specified that Lee near output is equals two
falls This is enough to tell our that
you want the activation function to book Okay
um the life sign nickels to fallen Life
signed out The peak was too tender again
Optional steps Um these are only to tell
our that Say I am telling here that
I want 2000 integrations Eso These two steps
are only to tell our that I want
to see the output after every tent it
race If you if you step If you
skip this step even then deal garden build
on But it will not show you what
is happening with each step So when I'm
telling our that I want the life sign
Step Toby 10 So after each then it
rations Like after Tenet rations after 20 after
30 after 40 So until 2000 I will
show me what is happy I mean with
the new network if you skip does it
directly give you win What Okay then um
head or not If city is nothing but
are telling you how do you calculate the
error function Okay Um so editor is a
real function is nothing but a difference off
the um actual value and the desired value
But how do you can collect it is
to see a sum of squares that are
and this is the best you can do
with neural networks Because with your networks you
want your error Toby positive so that you're
able to do a derivative off it So
the best thing to use an SS e
because some off squares Andrew So supplying all
these bana meters when I run this command
Okay so I ran this command See there
are don't hidden new runs That was what
I would had specified Ah with each situation
So I told that I can do a
maximum of 2000 steps but my unguarded um
stopped with 1 25 steps Ah so when
they were 10 steps when penetration that occurred
My error waas 2.869 after 23 years to
2.795 after 32 adios to 1.47 And finally
after 1 25 it rations my era reduce
to 0.354 one Um so the stopping right
area which worked here waas this threshold So
a dead innovative off 0.35 fun would have
bean lesser than 0.1 n hinted stopped here
Okay um toe want theater off 0.3541 waas
achieved This was achieved with 1 25 steps
so I did not have to do 2000
steps I got the output with each tenant
rations You see you can You could have
done a life sign Step off one to
see what Hap opened with meditation comes to
be bailed Eso with each penetration You see
the error was reducing and finally it came
down to 0.351 on this occurred in 17
seconds Okay Now if I kind of tried
toe lot the noodle network this is what
I get So see there are four input
variables Zeppelin with better lantern but ah I
have just one um hidden layer That has
it'll neurons Ah these are the weight The
ones that you see 1.83 on the top
Sick Yeah So this 1.3 that you see
1.83 that you see here is to wait
These are all the weight that you seeing
Um then zero The 6.6 that you see
here The minus 12 that you see here
Sorry Ah these are the biases So 1.830
point 76 the one that you see on
the black lines are the weights that are
being applied to the input variables The ones
that you see in blue 6.6 and 1.2
These are the biases which are being applied
So there's just one hidden layer with till
neurons Devices are being applied that both the
layers the base of the hidden the rates
are being applied to both the labs and
finally and output since being thrown out Okay
Ah One thing to note here is um
that if I ran the same command again
the way it will be different Okay so
if you're on the same commander the same
time with me even then this will be
different So far what is happening is um
I designed a score in is a neural
network model which is being created It's just
showing you that these were the four input
variables are these were the three output variables
and something is happening on this new network
is getting trained Okay um one additional step
which can be done Shores Um not with
the small later said that I'm using But
if you have larger data sets on one
another para meter that you can pass with
the neural network commanders Um this step off
the spiral meter called start rates Okay s
so what This is doing is that even
though with back propagation theaters are being passed
um and then the weights are being modified
But additionally you can also pass the weight's
off the first step to the seconds that
meaning to say um what their word was
a week that we're being applied in my
first step I can also pass them as
a perimeter and I'm back propagating ah to
the second step so that the same rates
are not being used So this becomes an
additional learning for the next book s O
This is not applicable when the Swan Day
doesn't that I'm using it but it won't
do any benefit But for the larger data
sets that a present the steps become helpful
And I'll show you how you do that
So you create another variable old weight or
you can name it anything on in the
iris on the school and was the name
off the model that I have created that
somehow who meet So I am passing those
weights to a new variable and then I
can create another neural network that has to
see him para meters Additionally I'm adding a
perimeter called a start wait in which I
am passing the old wait That is a
weight that I received the first Trish So
this is an endless no step you can
do to improve the performance off You're off
the Internet Yeah Get so so far with
Iris on the school Enough Just created a
model Now we'll see How does a model
look Okay so what I'll do it Ah
what I do here is ah with the
data set here I had used the training
data said to create the Mahdi Next I'll
be using the test data set to see
how my model the farms So um because
I had scaled my training data set So
I'll also be scaling my testing data asset
and try toe predict the output Softy testing
they So I'm using the computer function to
can't predict the values off the output Sorry
The testing data said I rescind the score
in It's the model that I had created
A neural network model that I created on
the data that I'm passing is the test
data set 1st 4 columns which are nothing
but the polenta with Atalanta with who can
still Finally when I predict When I tried
to predict the values of my test they
does it This is what happened Um now
since this is a scale data Sepp So
the numbers are achieved in this way but
you can again ah use thes numbers as
well to want to do a protection Let's
see How do we know that Um so
you see we had three species which were
possible Um on the results that I got
I get on These are 50 entries which
was in the test data set And I
get some scores for each off the species
Like for this particular ah flower I get
three values for each off the species Um
so the school the probability that it is
a set Oh sorry 0.1 The probability that
it is Wasim Khan A receipt of pine
cedar on one Where is the probability that
this poor Jenny Kai's 99 0.9 images 99%
So this flat which is in the low
number one thought Devon would appeal or did
Nika Similarly the flower which is in the
Ciro tent will be a set dosa the
flower It is andro 95 Phil Bill or
see color the flowers in 1 42 It
will be a can of virgin Icka This
one with veal or sickle So what I'm
essentially doing sure is I'm trying to use
the neuter network model No Predict what With
middle class off my flowers in my destiny
OK pass the test indeed does that I
passed the neural network model which contains the
learnings that it has from the trading data
set And I'm used to student you're in
This too predicts of this This is the
main step Do not get confused with the
other steps because I'm just doing my enough
formatting tow give you a nice looking out
there But but the step I'm tryingto predict
the values in the testing data set using
the learnings I have from the neural network
What It's with the results that I got
here I'll kind of try to compare it
to these other predictions that my um a
neural network model is making these other These
are the predictions that I'm tryingto make for
my um testing Gator said using the neural
network But I should also be ableto compared
to the actual values to see how good
my neural network is doing Um so Okay
Ah what I did with this step was
nothing but ah I tried So like I
was starting Utah radically here on how this
would be a virgin a guy and this
would be a Seto saw And that's a
grand will be a worse see color So
I kind of tried toe off but that
in words so that we don't have to
read through the numbers So thes three commands
that I'm doing Ah here are doing nothing
but producing this result from this result The
1st 1 should be about Danika The second
should be except also the thought should be
a war See color This is what I'm
doing here telling our that whichever is maximum
out off the three columns I want that
column name to come in my results So
this is the result that I get as
a result off prediction after my neural net
book for the 50 entries that I handed
my test data said these are the predicted
species off the flowers using my neural network
model Now I'll be using these 50 predicted
values on cans to beat Yoon Hee Shin
in me against the actual values to see
How could my neural network is doing I'll
cancel These are the original values which were
present and these are the values That of
course will finally compare Chaminda So you see
we have out of 50 Um so this
dismayed tricks if you don't know it already
we call it as a confusion my tricks
to see how good how good a model's
too So we see there is an error
off three here out off 50 that is
an editor off to be so 47 values
are being calculated Fine And hence the act
accuracy off this modernise 94% to tell you
our quickly again what I did here I
used my new network models to predict what
species eat off my flower belongs to in
the testing data set Then I compared it
with um the actual species which were present
And finally I get this stable which tells
me that I haven't accuracy off 94% Okay
So wait Well trained according to the error
whatever the uterus the end game is to
get the treasure lost 0.1 passing whatever way
it's possible So it is entirely possible that
when you run this algorithm the second time
um so if you remember here we had
done this with 1 25 steps Okay When
you run this the second time it may
take less of steps imitate more steps but
towards the end your result will be the
same Okay because the bottom is top of
the negatives A special off 0.1 us Just
to tell you I'm running this unguarded Um
fourth time since yesterday Ah once it has
run with 300 steps Wanted run with some
95 96 steps And once it is running
with 1 25 steps now but your end
accuracy with you be Ah the same The
same Um this thing the same confusion matrix
you'll get no matter how many times But
I'm the semen got at them because it
will use the special to stop and the
way it can be any Okay So offer
today session and dispute going on now Experienced
idiot analysis that will help you just see
which variable should be yours Damn it should
not be used What's the next session We'll
try toe um actually solve it to see
which Ah what does the output that eat
off the and cardamom rose Mm for a
thing Well that it set kept um when
the immune Tuesday doesn't it contains approximately $3000
or like 294 zeros And it has 35
columns Oh Dr Resistant Additional step to see
what destructiveness So we have a total of
29 14 photos and then we'll now go
to eat off the variables individually So when
I start off with the actress invariable wanting
to know what your wrists that these variables
have been arranged in an alphabetical order So
you need to ah if so better understanding
You might want to um three year into
these columns of that similar columns that together
I haven't done that But maybe you can
do that um arranging the similar columns next
to each other so that it becomes easier
for understanding eso we see here when we
start off in the attrition So there are
a total of 2940 employees out off it
Um 47 d fort had an actress in
Okay so that becomes for 74 upon to
95 to mine for approximately 16% off People
had an actress in and we'll see what
impact the Paterson off these 16% off the
employees Okay so we'll start off with eat
off the variables one by one and see
whether or not that variable should be included
here So I'm starting off with the aid
which is the first variable here Um so
you see the minimum ages 18 The maximum
is 60 Ah and the median Excuse me
The median is 36 which is somewhere in
the mid Um so the data looks like
not being very skewed Um we do a
box plot off it and we see that
it looks like a normal calls the EADS
So there are people from the ages 18
to 60 Um and the data is normally
distributed Now I'm trying to see Ah this
is another way I did a box plot
and this is another way to d do
a clearer history Graham We could have done
it using the hist function as well But
you can also use the deployed to do
a historic ramp It gives you a more
precise instagram to see the distribution off the
age Um and finally I mean I can
use indeedy plot to see the impact off
each So what I did here was for
the continues variables that were there with the
data set I'll try to see whether what
is the impact that those variables have So
when I don't want tea deep lot off
the age I get an open to something
like this So what This stands me is
the pink color shows the employees that have
an attrition and that don't have an actress
And on the Blue color shores Theo employees
that haven't actresses So what this tells me
is that off people who have an address
in on who ho had an actress in
our belong belong to the ead somewhere from
probably 28 to 39 the ones that who
do not have an attrition Ah have any
summer from 30 months to see 43 Um
what the status is that when you're seeing
just the age baby have been alone There
is a difference in the agents when the
people have an actress in our not so
people in the smaller in the younger age
groups are more likely to have not percent
compared to people in the larger IT groups
So this helps us understand that when we
see the impact off AIDS alone with that
person So age is an important way table
when we're tryingto study at person because there
is a significant difference with this graph so
that it should be included as ah variable
as an important variable when we're trying to
do the modeling Yeah Okay um next we'll
go to the second variable bitters business travelers
So it is a categorical baby a bill
that tells us um whether or not the
person travels for business on dhe how often
each other so out off the 2940 employees
that we have So we see that most
of the employees travel rarely and then we'll
try to understand whether or not traveling has
an impact on attrition So since this is
a categorical berry But I just created a
simple table to see if there is an
inn backed off business travel in actress And
so we see um the racial that you
see between no one yes Years off a
non Travel the ratio off to 76 toe
at 24 41621 38 and 1774 is 2312
This is almost similar There is no match
difference Let me go back to numbers So
76 my many fool for 16 reminded my
fun 38 I'm so in sound for debate
my tree go So there's not much difference
in the people who travel to see whether
or not they have an actress in So
you can't choose to ignore this variable because
it doesn't look like um it is having
an impact on whether somebody or not has
an actress in from the organization Okay eight
we saw has an impact but ah the
business travel does not look toe Aah Have
a significant impact with um the business travel
does not look to have a significant impact
that Trish in so you can two stools
ignored Okay Similarly we quarto next on the
daily rate So the elevators again continous variable
which Shaw's Theo today that written employees being
bid on a daily basis Ah so when
we see a somebody off it It'sa can
contine use variable the minimum being 10 toto
a maximum being off 1500 when a door
box But it looks like anomaly distributed the
and you can also see that from in
history Gramma swell huh But a good thing
to see here is if I know the
impact off the lead rate on our address
in probably the daily date does not have
a very significant impact Let me go back
to the history Cram a can to show
you to see almost all of the daily
raters are uniformly distributed meaning to say that
across all the 2900 employees that we have
the deleted it waas evenly distribute And when
I did a comparison off people who had
tried or not from the organization based on
the daily rate that is no significant difference
It is a very small difference here if
I zoom it out Um so this is
400 to 600 to somewhere around 450 or
so So this is not a very significant
difference compared to the dreams off daily rate
that I have That is not significant difference
in the daily rates off people who have
an actress in and knock so even daily
rate you can choose to ignore And you're
doing the oh Mark Okay with eat We
had a significant difference with David eight We
do not have a significant difference And also
we saw from the history Graham that the
daily rate is not very buried It is
almost constant The frequency of the daily rate
within the employees is almost constant So it
doesn't look Lake having already significant and act
you can choose to ignore the daily date
we're able as well Next we um go
to the department which is a categorical variable
So we just to see open to seal
somebody off it gifts So see this is
significant here with the human resources 1656 divided
by 2 66 on So this is evident
from the data as well Time trying toe
Show it more clearly Okay So when the
department as well um the ratio between no
and yes is almost similar I'll get the
people who try it wasjust the people who
do not try this again Similar so department
again You can still ignore because there is
no significant difference with respect So there is
nothing like people off a particular department have
more actress in compared to people of other
departments So you can't choose to ignore this
Very bless Well okay so then on maybe
Okay the waiting for results Okay so then
you'll see that there is a different significant
difference with the other departments So if you
see that for a particular department um the
ratio between no and yes is um different
for a particular department compared to other departments
So then you say that people off those
that particular department have a more after since
rate compared to people off the other departments
So that will mean that that particular department
has a significant importance when you had Ah
s So yes when you're creating a model
in that department variable has a significant importance
and hands it should be included But with
the current data said that we have there
is no significant difference with eat off the
in our department So you're OK to ignore
it Okay And um this is what happens
when um in the industry's when we're actually
working So this this is again our data
set with fortify variables but this is again
not huge We had actually worked with one
off the data set that had some around
17 be variable to something like that So
we need to Ah you know most of
the time most of the time get spent
in doing the experience Idiot analysis only because
of the models that you're creating like the
new network I created here is a two
line in command that you can execute within
seconds If the data issued it that command
will take a minute or maybe 1.5 minutes
to done But then it is again to
line a command that can be executed quickly
Most of the time that goes in the
organization's is doing the extra weight analysis and
that needs to be done for Eat off
the variables here So he opened Tuesday Pass
it I haven't done Also I had I
had taken an overview of what the summary
waas So this doesn't look like a data
which contains our flyers So in actual data
in the actor later that you'll see they'll
be out liars as well So how will
you understand whether or not it has our
players is by starting the individual variable Okay
You started the individual variables which l variable
you see looks like having an outland that
needs to be treated if there's a missing
value with it didn't within the data set
that needs to be treated And finally using
the summaries of eats off the variability need
to decide whether or not that variable needs
to be included in the day So I've
done that for eat off the variables Um
So since this is clear now a cleat
on through all the variables So when you
do this exercise for the meaning project that
you're doing repeat Ah this these steps Nissim
elmann ever eat off the variables Then you
also have this distance from home Uh so
that tells the distance off the employees home
from the office which is a number from
one toe 29 Hugh's own this out This
is already Oh you know in Theo Davis
well that people who have so distance from
home to your office a significant ah factor
and that is also evident from the data
Do you see people who have more distances
from home like um yes is the yeses
in the blue corner so people we'll have
more distance from home um are more likely
to have an actress in compared to people
who have less the distances from their homes
So distance from home is a very big
but should be used as a mom factor
for impacting mattresses Next is the education level
and the education field which are are categorical
variables so we'll just see a table off
them here So with the stable I'm not
doing the actor you know ratios here But
do the ratio's off these variables to see
if a particular education level if a particular
education level you notice Ah has a more
actress and compared to other education levels then
you need to include education in the hour
as an independent lady of them But if
all the education levels behave in the same
weather and you can choose to ignore it
similar is the case with education field as
well Ah if there's a significant difference between
the actress in off yes and no for
any particular education field then it should be
used at the size it should be It's
not now One thing toe see here And
you do different models Like for example if
you're doing Ah um random for the start
of noodle net book So even if you
do not do all these steps the model
will run So especially when you're doing a
random forest and you do not do these
steps the model will run and the random
forest and for that matter Carter also both
off these and guard Tums chose the best
variables by themselves Okay Because they use the
like our heroes is the genie in concept
So it it sees all the baby ability
says as well And um Toeses which variable
is caught between Israel is not good for
splitting So with those algorithms even if you
don't know the steps is okay but doing
them is a better thing But if you
don't do them is okay But with neural
network if you simply pass all off um
the weight tables at once If you pass
all off the wait tables the neural network
model our model will run but the accuracy
will not be caught So this step becomes
essential when you're doing a neural network to
achieve accuracy Okay Because with neural network there
is no particular Joel You know I'll guard
him to decide which were able is important
which were able not important Whatever the variables
that you're passing through the noodle network it'll
learn from all of the variables the back
propagation the aero creates the greedy and everything
will be calculated on stage Eat off the
input variables and it has no matter to
decide which variable is called Admits variable is
not good So then the model accuracy is
not very nicely So about very nice especially
when you're doing a neural network Does this
as a handy three step Okay if Andi
Unless you do this you won't be able
to Ah also see So there could be
some variables with a coordinated there could be
some barely able to talk in pure stuff
That could be some variables which can be
calculated from each other So you won't be
able to understand that until you do an
extra tree analysis to see like for example
I show you one off few variables here
for example this employee come so employ Count
is doing nothing but it this ah like
Shinto telling whether or not this employee's distinct
your just to take that there are no
duplicate increase So you know that this variable
doesn't make sense when you're passing your toe
a model because ah just by doing a
quick check Once on this particular variable you
can decide whether or not there are a
unique employees are not random Forest doesn't know
that So Random Forest will try to consider
in one or more off the trees that
it is creating Andan forest will try to
continue to 1000 variables But you knew yourself
No that is not important So I just
pass it that No Okay Sema notice for
one more variable which is Ah I think
already being here So you have UNAIDS variable
which is enough to calculate the impact off
It's Andi overrating anyway contains all my values
so you'll know that it is not significant
that and um for this doesn't know that
it does not significant Daughter doesn't make sense
So I just pass it that okay Because
in case it sees that it is significant
But you know that it is not significant
So it is advisable that you do not
pass such variables that um there's another thing
I'm showing you for an example With that
you should avoid when you are doing the
modeling In addition to the data set say
you'll have um salary off like you had
man Create hearsay You have salary off Done
Very salary off February salary off Mars and
so on till salary off December for the
employees And then you also have a total
salary in your data set Assumed that the
salaries for eat off this month are different
They're not the same Say this is 1000
This is 1002 100 This is 1150 This
is 1300 so on Um these are not
same And then you have a daughter salary
images some wolf all those salaries And you
have all the variables here Now these labels
are may not necessarily be coordinated It is
possible that for ah one employee probably or
the salary of February was more for the
second employee Maybe the Salvi off February was
lesser than done with either salary off mart
shooter for for any reason And then the
daughter told we would be accorded me So
if you see salary off Done Really sorry
Off a breeze on through the re off
march in the ST I said it will
not be cordoning but the important thing to
note here is that don't really salary contains
the essence off all these radia bols So
when you have a reader said something like
this it nor all of these variables include
us The total salary Okay so this is
an additional step apart from Correlation the society
off Don and Feb may or may not
be correlated here The salary off done on
daughter salary may or may not be correlated
here but then the totals are re captures
the essence of all off these variables So
if you have a data set something like
this ignore all of these variables include just
the total child So I I did this
for all the variables here Like we have
the employee count like I showed So employees
Countess just to see whether or not there
are duplicates in the data And there are
2940 employees and ah the count is always
one here So unless you do an analysis
off this step alone Ah you wouldn't be
able to determine this sauce This variable has
no importance at all And you should ignore
this Okay so I kind of did that
for all the variables for environment satisfaction The
second is a categorical variable and you can
decide whether or not to include that team
was for them But it was a categorical
variable Thing was for the are really great
here So you see um we are leading
this very much similar Whether or not there
is an actress on the our leader it
is very much similar There's no difference So
don't include this way table when you're doing
the mantra When More thing I wanted to
show you waas what we had seen or
Leo nous I'm doing this for all the
area built and you can decide whether or
not to include them Okay for the monthly
income So you see the somebody off monthly
income a tree and just from 1009 toes
20,000 approximately on my door hissed a gram
off it Okay so we've seen this when
I showed you the or air pollution on
a regression example We have seen this but
see the hist Oh come off Monthly income
is highly ce cube Okay so this again
should be considered Ah before you do the
modeling that they should not be any ce
que nous in the data because if it's
not normally distributed on the data will be
biased toe alone more from the monthly income
Like here Probably the monthly more people have
man clean commander inches off Probably 2500 to
7500 contains most of the people So the
model will learn more from the employees that
have started really interest between 2500 to 7500
and lesser from the employees that have salvaged
angels in the um other and that have
some reason the other rangers So the data
should also not be skilled when you're doing
the modeling So when I do a log
off it askew n'est ce get removed and
hence when you're passing this variable you should
not pass the monthly income You should pass
Log off monthly income as d'oh independent variable
before window What Okay welcome So couple of
things to check its when is the missing
values honesty out Fliers then see whether or
not they're correlated on the labels are correlated
then also see what I told you in
the example If one variable can be computed
from the other way the a boost and
used just run off them then also see
whether or not the details has our clients
But not if it has our supplier But
whether or not the date I it's normally
distributed or not If it does not family
distribute But um no a transformation and then
pass on the transformation as a variable rather
than the individual variable Okay Samantha radio Monte
Income you see has a significant impact on
the actress in the people who have a
small A monthly income and the people who
have a higher monthly income Okay I've done
that wrong to be agreeable This is again
already Denway variable It contains all realize that
all of the people are above 18 So
that that is a totally off non significant
variable Ah we see one over time Variable
can see the still doing that overtime out
of 5 78 on 2 54 and then
this is approximately 1920 So people who duo
over time have more likely hood off having
an actress in that 10 people who do
not all the time So when time is
a significant radium um the same thing What
we saw for her monthly income also applies
to person salary hike Um so see this
again is a heavily skewed data set so
you need a tow through a transformation off
it before you actually pass it So I
used this a box cutter X test which
we had seen earlier than we did our
regression So I used the random um study
the Lambda value Sure And when I blocked
it transformed value off the Boston terribly high
The unis looks like having being removed So
then I instead of passing the bows and
salary hike I should be passing a one
of a born poor sincerity Hi cousin Independent
radio gets in It'll not for all One
more variable here was was for standard ours
which is 80 for everyone So you can
ignore it though that for all the variables
Oh okay Oven Easy way to check for
the missing values is a command that of
port here I think you can do it
for all the variables One by one But
this is one simple command to take Ah
the missing values all at once Um so
when I run this command I'm just checking
whether or not there are any and isn't
any off the values And I'm creating a
matrix based on that So it shows you
for eat off the come maybe give us
whether or not there are any missing values
Okay so show 00 zeroes in all of
the values means that they don't know missing
while using and me off new waiting this
Thank you Thank
