convolution neural networks make it possible for machines
to visualize the world like humans On That's
have become an important concept alone while working
with configuration thes convolution neural networks excellent classified
image data understanding the importance off this way
This tutorial on confusion Now before we had
our concession I like to inform you I
said we have launched a completely see platform
called us Create Learning Academy where you have
access to free courses such as the I
Cloud and it's still marketing We can check
out the details in the description below Now
it's our planted The agenda will start with
a simple case study on your networks Then
we'll have an introduction to image processing After
that will understand the concept of the documentation
And finally we'll have a demo using CNN's
The new little that you will see now
might feel little high because we are talking
about CNN Soviet on volition nearly rocks But
more than less the concept remains seem so
majorly The German people I say you'll get
multiple sightings on what exactly is leaking Lewin
where and all you you need to use
them I just try to understand this concept
lately Um sometimes what happens is there to
say this is your real okay So usually
really was like this All right Sometimes what
happens is that the network that you are
having is highly in your metal So usually
Ray Lewis is used when there is a
non union Later when you want to push
it unit extra known in your part isn't
right So when the linearity off their data
because usually we don't go ahead and go
the linear checks and our last thing before
implement If in case you feel that your
model is even after using the loo so
your model is not performing that great In
those cases you can try using legally leaky
Really What it will do is it will
try toe clip that gray aliens after certain
points So others even if you talk about
the slopes that we are having it will
try to control the slopes such a way
that it does not go beyond certain values
All right so that is where you can
usually kill it So if you take an
example here what I have done us I'm
using a convolution conference Music convolution lee and
in that convolution there I'm using saline Charity
is my activation function But just to keep
a non linearity in my model I'm having
a leaky value with a cut off value
off I say uh already saved So value
off around 10% Yeah So this is what
it this for Usually the use it on
on the convolution side when you want to
build a good stimulation it on image processing
and all All right If you want detailed
uh notes onto this I will try toes
search the official documentation and share it with
all of you There are so many different
versions off Are the alterations off these model
seven Okay so this is one example there
instead of using directly Lou amusingly So um
OK so I think um we'll do wanting
in the third week Every will baby sit
than your element baby Sit in the front
window Featuring is ations and mountains We'll try
to use legal wars in place off Really
When we will see how it affected are
put on how the performance What's so today
I will show you two different case stories
off classifications Moral so whatever the that didn't
machine learning We can replicate the same thing
in terms off Stadi relate once And then
what we'll do is we'll try to discuss
on a possible regressive Yeah So it's a
part of third week Anything but no issue
since we'll have we have some time Let's
do that Us Yeah So the first case
study that we have sa strengthening police Okay
this is a financial case city that we
have on What does it mean is if
these are the attributes off a customer see
Detroit as a customer and thereafter my producer
bastard So I mean I leave this on
you guys have you won't do feature analysis
Is poor relational that it's not a school
as off now but yes definitely Before starting
over the great I said you can do
whatever you'd be forgiven That feature engineering Nothing
wrong with that Yeah So finally what do
we have is we have got a target
column which we call it exit so that
the customer is moved out He still that's
what we have supposed to classify So what
about what If you get this under your
classifications problem And what if you put that
mural And on that let's see how really
so before coming to that some important libraries
So we have care us a sequential model
We have to if you don't mind what
Cara start layers then we're home or dense
ification That means every time you add a
layer you have to define it using dense
Think about from that one of the less
everything it seemed on the most important one
not to forget here is hard and quarters
now what exactly you mean by heart And
according will see down the line and why
it is important to see that So these
are the libraries that we have uh moving
on What do we have in our data
Set The shape and size is basically this
So we have around our 10,000 cross 14
on the side of the great eyes and
are importing games It's ah minimal medium face
Data said looking at the data leaning part
So I think doesn't all we are pretty
obvious for so we'll remove certain columns from
that and remaining will store it under the
name RBS for example Next is for your
target imbalance Yes there is a garden in
balance issue on if needed you can solve
It else is an irritant of I don't
think so This will create a much off
issue here Okay fine Next thing that we
see overhears the award under geography So that
is a Colin Powell geography Under geography we
have got three auctions here France being in
Germany for example So if I do and
heart according wonder that what's gonna happen Germany
is going to get a study France's under
that zero Germany's gonna get one and speed
is going to get Yeah somebody for uh
So just to showcase how how close the
readings are the readings are like these two
are almost similar This double off them All
right good Next is let's try toe Define
extend way So this is my uh independent
data This is my different in data on
dumb is that what I'm doing is I'm
trying to convert all off my categorical daytime
to some kind off numbers using my label
in court to be very frank It is
simple Yeah So you might feel a little
beer What I would do is when I
deliver the school to you know I'll make
it more simpler I will remove the extract
things which is pretty confusing here So basically
it is nothing but converting your categories to
some kindof numbers Zeros And once if you
observe here has we predicted France Spain and
Germany We got this numbers and there was
no problem Meal familiars s bottle Alphabetical order
Regards yours once right Okay good So now
I think this is not needed this unwanted
confusion So what we're doing now is we're
doing a test in science spirit So I
say 70 to 80 20 split I'm taking
some the Iran and straight I'm thinking After
that we say we're performing some kind of
scaling function Since the day does little up
random for example I have done some scaler
Now there is one more thing available within
a new religion which we call it as
normalisation or bash normalization That is almost similar
to this We do it after every layer
now that comes under the contents and hence
I'm not sure the same thing here Otherwise
you know use that consequence Okay So victory
haven't show you how exactly you can do
normalization under each layer as as a default
Param Eter after each layer Okay so this
is what we're doing Scaling on our training
Investing later Who is that If you look
at the split 8000 is to 2000 is
over shape So next thing we're talking about
is our mortal So the first thing that
we're supposed to get in as a sequential
morning perfect Next thing is we're talking about
the input dimension Sort of to check the
raiment is all you need to do this
You need to go back and check the
door Lamar Oh Leah Reliable that you have
In our case we have around living So
we have defined it There should be 11
inputs in a one network Okay Input distort
11 So the first network that you see
for the first your neurons that you see
there 11 in number Yeah And uh next
thing is it six off them are going
So that means 11 off them are connected
to six off them in the next layer
That's what it means All right And the
before activation function we're using here is radio
Good So this is your layer number one
So I will again say this is not
the only writer to finally but yes to
get started That's the easiest widow defining your
leg Okay And why have I done it
in different cells so that you get comfortable
And once you're comfortably push everything into one
particular sense that's easy Next thing is a
Zycie There are um six units off layer
So what are we having out That 11
of them are connected to six off them
All right Can you give me a minute
Um that isn't the issue here Yeah one
minute Grace Okay Actually this should be in
sync If I have defined six year have
to refine six again otherwise it will be
mismatch So as I say if I'm giving
out put a six They should be six
number of neurons present in my next layers
Citing the realization here Next us your activation
function I'm using signal right now You may
ask point Where did I change it Probably
you can say post Really The leader would
have become a very positive data So in
just in case toe clip them between zero
and one Can you seek my orders The
company network could really find Andre So if
you ask me what is the optimal way
to do it I will say that is
not normal weight All you need to do
is mix match all else different on your
day as the concerts Whatever input there having
just observe them and try to use 99%
of the times really will work Okay Certain
cases where you have extreme negatives and positives
in those case Sigman Now why did I
take sick martyrs If you remember the starting
off the court we did standards Killing here
prince isn't is a skill My greater definitely
We're gonna help fosters and negative stricken So
in that case you could have started a
sigmoid and then ended up with three lords
Not a problem All you need to do
is mix match and check the accuracy That's
okay Next thing is we have to identify
the last player So if you observe if
somebody can pinpoint the first layer is having
11 neurons That means we're having 11 Iran's
like this The second layer that he didn't
let having only six off them And the
3rd 1 should have How much If somebody
can point out this How many glasses We
have 01 So you have two options here
either I say I will have two layers
here for two neurons here I'm sorry One
of them is representing zero Another one is
representing one In this case I will choose
The activation function is soft Max if this
is the case But what if I don't
want this way There is an alternate way
to do it Just define one neuron If
this is active if this is activated it
is one If it is not activated at
zero even you can take So if you
get that is the case you don't need
Ah soft Max Because there is only one
There's no class of Yes In that case
you go ahead with a signal Functions in
this case just to give you an example
Have taken the output layers Signally I really
find one neuron is in Or put signorile
function and the corner Okay What do you
mean back on initialized Colonel Initialize There's nothing
but the way we put round number It's
so I'm saying we don't care about it
Whatever way to put it back Propagation will
learn There are 30 another method using which
you can't even define your feet You will
see next week radios how to optimize this
meets once For now Even if you don't
find this by default it wouldn't be a
uniforms All right So when I give this
court to you guys what do you guys
need to do Is just something from your
side is your chain district two and put
here soft mats and then try to devalue
it The model just look at the difference
of accuracy is that you get well it
and issues with the morning creation It's a
very simple three line or more 12 entry
That's it Not a complex moral order But
why would you use soft max often accepting
using me when you have multiple Um uh
classy fires right here I think Yes So
just to give you a variety that if
you have to off them if you don't
want to use soft Mexican you sick models
and later on whatever because this is a
regressed output You people given that this is
a regression Yeah So you convey that aggressed
output and then put a manual filter outside
So if you see here I put a
manual for their here These are manual for
I'm saying if my white bread is greater
than 50% I call it one all psychology
So false means he will not quick room
in civil court This just a variety Just
an option I'm sure Yes definitely soft Mexicali
Ford when you could use it Okay No
one clarification with the input bank we have
given in four by mus 11 right Yeah
but say makes you know that according like
the male intimate become zero wanna under this
also becomes you know or right then there
will be a coalition Right Vivid me We
made me to drop some columns Right So
is it just to have 11 more We
don't know No I don't think so Correlation
really affect much over here to be very
frank because here we're not believing any kind
of relation between variables all all that All
what we're doing this There is some randomized
rates Well true And we will multiply those
randomised rates with our imports We keep doing
this and we keep correcting this rate Still
we say that whatever we're giving it the
target column is matching with our okay You
remember our older machine learning techniques s we
am or Kale and our decision re There
were some relationships that we defined here There
is no relationship but it is OK talking
my application and corrections that OK but still
it's a very good matter if you people
want to follow That's a very good matter
Always the data that goes on no should
have least amount of multi culinary I agree
with that but in our nuclear talks it
will not be in effect So you don't
have to worry much about that Data cleaning
partners three The new let will will be
getting benefitted The data that they needed we
didn't want Um OK that's difficult to answer
in one week But um I have never
thought this really But yes I asked for
me That should not be any problem even
if it is highly core Letter also should
not be a problem to be the flick
so I would not say that benefit but
yes it will be a neutral ran off
stuff No no No word No bad Something
that Oh yeah Perfect Correct So this is
one type of neural it Okay I'll say
it's a very simple one So this is
very simplistic So will not do this just
for as a first You know court we're
starting up with this is what we can
define now coming on does that This is
your neural network architecture Now it's still empty
It's not It's not frilly it so to
fill it up our toe first of all
define a back propagation What we do is
we defend optimizes Please remember And we have
different dive Optimizers We have got stochastic Grady
and Descend We abort Adam were what Animists
Prop So every session I'll keep showing you
one by one The last session meets me
So Adam decision We're via seen SG stochastic
Grenadian descent Now if you ask me what
is the difference The way on the algorithm
deals with do l by door Fritz The
manner in which this is built is different
in Adam a difference It is different in
ste It's all about how the Canadian dissenters
cheap All right so that makes these things
different This is a genie for no The
most common ones are SG and item and
out of those two if you ask me
Adam is right now You knew Assume it
works for anything right Next thing is you
asking me for a loss Now the various
type of loss I will limit with us
One of them is called Uh I mean
some of errors Another one could be a
difference Another one could be an entropy Thurman
could be cross entropy for 20 If you
have high level of categories let's see your
pour 10 categories in output In that case
you can say categorical cross in trouble Yeah
So we will see down the line one
by once If you remember in my last
session we use categorical crossword drop in the
session at the same Hughes He mean some
of Paris now Why Why did I choose
this Please remember the output function that were
refined is nothing but a regressive If you
see this output function in that case we
don't need it There is no category in
one year But if in case I would
have defined here's a soft max I would
changes to crap in it A simplistic good
Next thing is what you are focusing on
your model is focusing on Do I say
find them okay with actors If you want
Positional if you'll recall or if you want
f one score whatever usually rewarded with accuracy
it some other resorts We So this is
what is about back propagation and we compile
it so that it gets a times with
your modern And after that it's always good
to check your model So if you look
at this this is one more So our
current murders The first layer has got 11
inputs but it has got six outputs So
if you say 11 6 or 66 plus
six raids are going from one layer to
another So if you add this up you're
one of its 72 was a number So
this is how you can compute the Panama
does It does not mandate that he could
do it Now why do I say parameters
by today Sure this calculation is tomorrow If
you guys are giving an estimation you know
for So this is from management perspective for
giving an estimation saying that what could be
a possible cost of training this network we
call these things as a cost white Each
one is a trainable perimeter Please remember that
Look at this Now if you want to
train about a murder you that is because
in order under that so these architectures don't
take much time But the court that I
showed you earlier if you remember like sure
your core on CNN convolution neural networks these
architectures takes hours off the hard way That
also high end hardly be quiet GPO's Yeah
So these things are around 30 to 60
GBs off rans and or lists In that
case if you are on a shared basis
our client also does not want to grant
humanity abuse In those cases we have given
estimations In those times this might be this
information might be useful else It's of no
use All you need to noise There's a
ready made think white trainable parameters from here
It's so you can pick up the lead
All right so in this case I would
say it's a very small morning so it
should not take much time Toe get trained
All right so now I've given some examples
of Optimizers Onda way Have Aramis prop Usually
we use it in computer vision and are
not so It's some noise right now for
regresses and class offensive can focus on our
Dev Industries has one more thing is I
just need to know clean up the score
That record was it looks little confusing Sometimes
it was large off courts What I lose
I'll try to optimize it and push it
into one cell along with that comment So
that's easy for your life Didn't so many
comments off more used to be very flat
Okay now moving on wear defined the model
We have done the back propagation We have
seen the model How it looks like next
thing is to check Uh huh He said
ah to fit the model Basically so fitting
thing What we do is we define more
on what you want to fix it if
possible If you have a validation did also
just put the nation data So this is
your training later This is validating Next has
come into number off He pokes Now there
are more people ways in which you can
choose this eat box So let me show
you 11 week Uh so the first ways
all school way What we do is we
start with many box So when you start
with any box just observe the Grady in
descend Or is the loss the increase or
decrease in los happening for first time writers
If you observe year there is not much
difference Even the accuracy is not getting changed
much So what does it sure is No
matter if you do 1000 ik walks or
10,000 box it's not going to change much
But if you observe after certain level off
a box let us after 203 and Reeboks
it is going about 80% from 79 to
80 and after that it is a very
good gradually increase So like that sometimes you
have to do it manually There is no
formula under that to start with a lower
number of feet box and then carry on
all else If you don't want to do
battery I have an Internet way off doing
it So if you observe you what I've
done it have taken a very simple plot
off my training accuracy What's is my evaluation
Accuracy The North's are nothing but my training
accuracy and lying is nothing but my relations
Let me get us Yeah this is a
better cough So if you look at this
the relegation accuracy is increasing up to some
point And after that point or after that
e books are basically exact city presence people
This is present trackers Yeah So after some
point I can say that Yeah it's almost
holds on down the line whereas the training
accuracy keeps increasing Do you see that Today's
what is this thing Guard Can you name
this scenario Are you able to visualize this
and name it or hurting So this is
an over fish So this particular graphs will
not give you an exact idea of how
many books but at least they will tell
you that for this model training and evaluation
are in hand to handle it Pop number
two And after that there is a use
increase So don't train your model much Go
back and change your network so that this
error comes Stop You know this this this
distinct comes No So what I do now
is I go back and change my model
So it's OK if you don't get it
This is a convolution Neural network and 18
my model If you observe this this is
how it looks like after doing some changes
Now what other changes could be number off
layers Could be number of Ben slayers activation
functions And there's something called dropout normalization Zal
those extracts that we will see down the
line But if you observe now under some
point our testing on regulation accuracy is more
than training But after certain number off minimum
E box they are kind off handing her
So you can easily say that for this
network at least you need to go eight
or more than eight people All right But
I hope you people get the point If
you don't want to use accuracy you can
use lawsuits Its opposite off But I feel
good with visualizing accuracy So better do this
for the in between Hidden alias Is there
any good practice to see visualize How many
Uh yes Uh huh You did Yes I
will not recommend that there is a good
practice but yes I've got hold off some
off the papers So where they have tried
to push some formulas Now some of the
formulas goes like this Say If you have
got 11 dimensions as our import and say
three years your soft makes or poured you
add them up So you get 14 and
divided by two At least that many number
off it unless you need to have some
off The papers say like this But when
you apply it on some off other data
So when you just read the paper it
has been applied on certain type of data
It works well but might not be the
case in on our data Why Because it's
all about putting the vague going back and
going front and back So I will say
how to decide this How do I take
this challenge Fears The first thing I see
is cost because if you have more number
off Italy S C your training time and
costs will increase You people agree with that
because we'll have more parameters so more back
in front property of to do ours You
can see more e books unit Yeah So
if you have a classifications problem say the
parameters and output they're kind of comparable numbers
In those cases two layers are more than
enough in really right But If in case
you had let us 100 imports and some
say 10 outputs now they're not competitive So
in those cases what you can do is
your 100 input might go to some 201st
layer Then you started using 100 Then you
say 50 Then you say 25 20 factor
today So in those cases yes you leave
more less But if you apply our formula
here 100 plus 10 years 110 divided by
2 55 That means you need to have
55 layers the comfortable I probably Jupiter every
day doing this fun turned back propagation if
you have 55 names so this formula more
or the less does not work much So
again I said tried to use this kind
of logic how free you are on the
cost side effort how free you are on
the training side off it your fluid But
one good thing is more the layers no
more The parameters better is your model You
people agree with that because more heat box
you have to do to train your pants
Nobody gets Ah uh We might end up
in the over fitting as little agreed How
really balance all the thought So one off
my learners from other batch worry When he
proposed this I said Oh he told me
Can use great search here I felt fine
You can use good So it's not a
problem in great side Say you put some
values like this and you start your goods
such But just imagine the time it is
going to take for the good Such to
end and show some best friend Yeah yeah
I got it So that is a issue
with this All you need to do is
you need to be a little patient Under
that you need to do more and more
data sets So once you get on to
it it doesn't You will have after something
point You have a hunch saying that Okay
If this is 100 let me multiply this
by two Thank you Be waiting it back
orders If this is 100 let me start
with 50 and then keeping using 100 then
come back So once you have this kind
off attitude of multiple mixed match combinations at
the end definitely you will come back later
Network But so far There is no formula
Definitely Which gives you a perfect number off
He pokes I can give you a reference
off some blobs that I have and before
them for your reference But sometimes they work
Sometimes they don't And the he didn't lay
it Also is there any formula Like what
You said that that's also a big trial
and error on our arm Needed layers toe
You said 100 plus standby to worsen country
So how do you How do we decided
it sold six An average hidden The Yeah
So let's see that Let's take an example
and let's so not it example for hidden
Uh that is it Yeah So let's say
you have got a day Does it We
just got 20 inputs for it And the
classifications in our quarters around Safe fights for
example Yeah So you need to design Ah
heaven layer in middle search area that this
should be satisfied So what do you do
it if you want to go ahead with
trial and error matter what you do is
you start of it Since I say the
difference between this and this is pretty high
start rich tool is okay And usually what
we do we diminish the list Diminish in
the sense If this is 20 I will
say my next layer could be say hunt
for example 50 That means it has 15
neurons in tow Divide this by to say
20 face and from 25 years Shrink it
too fast All right 345 Go and do
your e box and check your accuracy Now
how to check the accuracy Ease Check the
difference between your first iPAQ Secondly pork 30
both for the profit Favorable So if this
if the accuracy is not changing overall they
can say that Yes this is kind off
a good morning because even after doing front
and back propagation my paper times it's not
changed If the accuracy say less than 70%
in this case then there is an issue
in this Then what you do is you
increase it say 200 make it 50 The
2nd 1 introduced on Morelia which you call
it is 25 for instance honest if you
want one more 25 you can have one
more 25 late here Nothing wrong So it's
all about you know trial and error like
this most of the times By first day
life itself implicated If you're chosen the optimizers
and all perfectly they should not be a
problem Sometimes we get onto this kind of
trouble It just that those points we need
to get into violent slicks All right But
how do I do you go 250 or
100 The fist still suddenly random members damage
to be very frank And that should be
a lot higher than uh yes Okay you
know why Because if you are if you
if you look at our fully connected Nuland
what does it How the diagram looks like
this Each neuron input neuron Okay say there
are two off them Okay Just to save
some time in space and there are three
of them here What is going to happen
ISS This fellow is connected to this This
fellow's connected to this and this hole is
connected to all three of also This guy's
connected to all three of them Now what
happens is if we take the same number
off a box what will happen is sometimes
some of these letters say this is zero
and this is one So in this case
what's gonna happen The input that is going
to go will be always You agreed So
if you have same number let us say
you hard to off So what's gonna happen
if you have something like this There are
chances that both of them are zero We
are going to end up with C And
if this is zero the further propagation also
will be easy So it's always good to
have some extra Chinese There could be by
chance that could be one more which could
come and sparked them right Could be a
possible teary behind But again I would not
say the optimal Later you can have two
Indian ports You can have your first latest
trendy also nothing wrong All unique pick is
your e books How they're the here So
don't directly go with 100 box and wait
for the first time in your under nearly
go Go ahead with many books All this
uh go over 20 box Do this plot
this kind of plots I add discord into
our current do This kind of blood will
come to know Should I go more Norma
I think this is gonna be over fertile
You can predict it immediate After that go
back and change your lifts So there are
certain companies now who are working on this
concept called Ordo deal so that what is
kind off working on how to predict optimize
violet office once is that is out Anything
We could have some Some relief Oneto predicting
Thomas Yeah my sedition ists If you're building
a neural network make it a little complex
complex incense How Morehead Unless so that the
chances of feeling would be very Lis Yeah
if you have more Obama is also chances
are war footing also very so bored that
this sudden but yes citing wants to design
Doherty off them You get hold off on
it now since we are given this is
a regressive What did you get this regard
These things is an output Now all I
need to do is I need to have
a soft artificial filter role So what we
have done we're saying if you're predicted value
is greater than certain threshold while you it
should be drew an institute before it's just
a bread eggs zeros and months that call
it So please remember if you have a
classifier output should be soft Max if you
want to make the same thing as a
regressive use anything except soft Mexico for good
So now if you take this particular case
study and you can go back to a
linear regression data sir all you can go
back to any of the regressive later said
that we have and just go back and
try to compare your deep learning with your
linear regression If you remember we had done
some courts under linear and logical English Yeah
so use the same rate as it took
practice And then once you guys are comfortable
building this lives every year some more complex
data sets complex in the sense they have
What more variables on it Good So this
is it on uh one portion of stuff
from X I'd actually the socks off A
tuition function has been designed just for the
final earlier in this case If by definition
of course yes this is probably in just
a while Question by went up like this
icon to the existing functions Orientalists Okay See
if I have to use about existing functions
Then you have to design a filter like
this Did you see that We designed one
small freaked out here way exclusively Put a
filter outside the neural network to do the
process What if I can use an activation
function in second filter Yeah So it's just
an additional facility given to us which we
have you know hype it up And now
we are completely dependent on soft drinks Whenever
you have a category different software it's okay
Yeah You any good My are This is
an industry convolution cord Yeah we are doing
some image processing here If you look at
the output not good by default Soft max
And it works pretty good So we don't
We have no way off choosing any other
uh already say uh activation Here Call it
So whatever you're seeing now right as a
fully connected Euronet look down the line it
will be converted like this It's a convolution
Your little Okay So I will not say
again that disease it This is the ultimate
deep learning after doing convolution And if you
come back you would feel like it's very
super So these all are basics So that
you guys could do the scandals All right
Very less people now in industry use Ah
what you say They're deep learning for regression
and classifications Anyway our machine learning algorithms are
doing good on So let's not spend our
cost on hardware under the real reason why
we're doing or learning the planning is so
that we can train your eyes on your
computer vision And so this is a computer
vision topic All right Okay Let me not
confusing graze in that way But this is
a more or the less so please pick
up some of the older data sets and
try to use neural Tronto coming So this
was one example from our current scope is
the maximum we can go Let me show
you one more case Study uh Greek learning
We're on a truck Do Yeah industry will
use I think soft Mac So that you
get the difference between both of them So
what do we have for the say We
have got a date Assert eso Well not
you See guys one morning please remember some
off our courts from now we'll have a
reference to Google Collab That means they are
asking you guys to go and run Call
up If you want to kick start to
let me know I would show you What
does this mean Usually you will need Collab
from your next morning Not know some off
our courts might have a reference to collapse
So you can ignore that line and commended
otherwise is going to give you and never
ever try connecting Dr Sorry for lab It's
ah Jupiter Like it is going to end
up in a big mess on just to
question your eyes I already for murdered my
machine twice because of that Why did you
use collapse All you used to be just
one of them all right to these other
courts for connecting it with collapse We not
focus into that Now what do we have
today is we have got some some data
Uh let us say some bad amid us
around 28 kilometers from on Each parameter don't
see is that whether there is a fraud
detected or fraud is not it It's something
that so if this was not there it
was a regression problem But since we have
given a class we have got some kind
off Uh where does that uh target available
from class over here All right so the
data processing was a Similarly I would try
to meet this court up This is a
little older cord and try to make it
a little you know need and more presentable
So all you need to know here is
now Yes This is what is my test
in training data So this is my training
data This is the total number of columns
that I have and this is my target
data is the total amount of targets I
have now looking at this and somebody tell
me what should be my first layer Pretty
simple Obvious Yeah Okay I'm talking about the
total number of neurons So if I wondered
there develop on your left out of this
state 29 Perfect off So just that's the
reason me showcases someone's you guys a comfortable
after that You you don't need on through
the screen dating It's really primitive So now
if you come to the layer part offered
if you observe I'm having 29 inputs given
to my first layer These 29 inputs are
connected to 64 of them from the next
year So why do we write 64 years
so that there are 64 different weights which
are ready to be pushed one to the
next layer resort Winning That's all right Next
thing is activation function by before they used
real here Same next day And also I'm
using real You might find these two new
terms I will explain you after the score
What are they This is a topic from
week number three We will see what are
these things for now this is made clear
This is my hidden lip and out goodly
that I'm having is an activation function So
he's ah soft makes letters an activation Why
do I have to Because I have zeros
and ones doesn't know so either you use
my last logic are used this logic forever
So that's why I have given an option
here Either you use if you use it
as one It's a regressive If you visit
a store it's classified That's it's a very
simple network All right straight forward Moving on
If you look at this next thing is
I have defined my optimizer So by default
should be Adam on Also it should be
So Now what I'm doing is I'm I'm
showing one of the hyperba Ramadan stitches available
inside our optimizer where you can choose the
learning rate Yeah So the learning rate that
we have chosen is very low here 0.1
for Adam say and the same variable I'm
initializing here in my compile ish more lot
company So now I'm seeing Compiled what Combining
optimizer Which is it Adam orders If you
don't want to do that you can do
the source If you just put 1/4 on
red it will mean the state there You
don't have to define this place All right
so both options label next is what is
your loss So in this case I'm saying
it's a Binali Why Because it's yours and
one's only too often So it's a binary
reader and cross entropy If say it was
0123 then I will sick categorical underscore cross
and because it's a highly competent and the
matrix that we're using is nothing but actors
That's usually this optimizer does not change It
remains almost similar right Next If you look
at the summary of the morning This is
how it looks like that at 2000 one
won t trainable parameters And there won 28
nontradable Any idea What are these Why do
we have a non trainable parameter You the
means the admiral Okay And arithmetic problem It
is in a sense if you're kind off
near to that if you can allow word
alert means medians or no Um okay All
right E I think the importance coming twice
64 people 1 20 You cannot train Is
that something Later So that Christmas Okay Could
be Could be possible Else didn't take a
logic like this Okay One more thing is
this is an order court Just give me
a moment Base I think this is not
reader Let's let's let's try to read and
generate the latest I'm sorry Uh all right
And it is a little slow Okay So
anybody are working on this stuff it might
become a little slow Um yeah I will
start from here What went wrong here Tuition
function is soft Max A non activation function
soft Maxie s because it should be small
All right So the model is defined Then
we'll get the optimizers Okay Now let's going
chicken again See It changed up Now you
see no animal parameters as you So usually
in a simple neural network All of them
What you give us an input is a
trainable All right That's the reason I I
supported any shoe here Now when you come
to something like a concept off convolution neural
networks and transferred learning and all you might
find a split between trainable and nontradable parable
There are chances but usually in Ah simple
neural network These remember there should be zero
If not he's going to recheck your model
Also there is one question doll a few
days if you observe here these are our
dance Lis Okay And you you might observe
it is showing me for 56 metres near
just three over here You know why is
that Because this morning was already run earlier
And on the top of that I'm relearning
That means the model ISS scene that I'm
adding one more densely It's not what is
adaptable Morning It's a singleness More so if
you keep repeating this thing No again in
a game you will see Lord off extra
against lists of year which is not a
part of a NATO operation Right So in
that case what you need to do is
you need to kill the colonel and reload
it back So that will start fresh from
densely number one I hope you get it
All right So please be careful with that
Don't keep the IV trading and relearning it
It's gonna on wonderfully populate your Electra collectors
Finally when you foot it what happened Have
to what you have given an airship Is
one okay That is weird No it's going
chick Okay We have not done one heart
Including here That is issue We have not
done hard And glory is Give me a
minute Really Do that also Okay Very old
rd doesn't a I don't want to show
that much but yes let's Let's let's do
a heart And horny give is my heart
and quarter did a processing training Harris So
where's remember One thing is we need to
convert whatever we have in as a categorical
data Always So let me heat on it
You tryto really cheap extrude ahead Why did
uh did a ship okay So here we
learned this Not a problem He processing normalize
this After this step we would save the
new heart And Courtney Now what do you
mean by heart And 40 even though it
is in the form of zero and one
But if you observe as for a neural
network what without good of new leg looks
It would be something like this always right
Right So the output will look like this
It'll be uh matrix like this But if
you observe here we're having a single digits
So what he's saying is neural network is
expecting to us as an output layer but
you are giving us only one off That's
the reason it destroying us an opera So
what we'll do now is well do it
hard and quarter under that So let's try
to say this is nothing but your white
dream all right And on the top off
lightly we will see Or then Gordon my
weight It says the same thing will do
it for White distorts Yeah you know Okay
I need to define the practical Also Excuse
me a minute to get us Uh where
is my ready Yes Teoh good So now
if you observe this has changed too Perfect
Now it should look I Yeah you see
the model is started up on It will
not take much time These you know smaller
less are pretty simple Soviet given on 700
box And if you observe that could see
what we're getting is Newdow Ah Val Elish
Okay so now just to check what we'll
do is just toe bring the validation accuracy
Also let's tryto incorporate the validation data so
that we don't have no need to anything
And we will get to know whether it's
ah over for dinner All right I just
need to check with Skerritt Yeah let's read
of this So if you observe the accuracy
that I got for training is 99 the
same thing for valuation is also maintaining All
right so it's a very good model You
don't have to go ahead and add any
more lesson already This is working pretty fine
as a uh plus All right Good The
number of the boxes 10 on that sort
of India border So the end of tiny
box we're happy with it Perfectly fit more
the second way off checking It's you can
you can really can run the evaluator part
of it So if you if you see
here what I did was I I did
every violation here It seems if you don't
want to do that there is another matter
where you just call back the testing data
and call your model and evaluate Gilmore longer
testing you will get the accuracy here I
say this is better version so that you
can track people to eat Talk What is
the training Basically All right So this is
a little old school You can remove it
if you want And finally if you want
to print the confusion matrix this is how
we do it again This is your old
school type We don't have too much focus
now on confusion matrix Mom it's not gonna
help us much So this is more or
less a very simple classified Eikenberry like this
I hope everybody got the point off doing
this Yeah in last chord we had of
the GREss So it was not asking us
for output to in this case it is
not a regressive So it is explicitly telling
me you have to define this A stew
and rice The network is gonna fail Why
It is expecting to add an output Why
Because we have returned That's the reason we
have to do one heart including so please
remember Don't forget the step It was like
if you remember for unsupervised learning a normalization
was must like that If you're doing classifications
this is must good Any questions under this
simple pretty straightforward I fish in a FEMA
your team Just just one thing Basic question
What is a convolution It But you selling
with sometimes and able to read something out
But I don't think we've covered that So
what is what Is that difficult to explain
It didn't me Let me spell it out
for you It's not Say you have an
image Ali This image is one megapixel image
for example All right Now if you want
to put this into our neural network let
us sit with your work All right Can
someone tell me how things may say this
image is a cat inside it And I
want to classify that there is a catchy
I want to identify if we could see
on Linden and YouTube you'll be seeing people
are putting ah square around an object and
showing no KBR Then sometimes its computer vision
object detection you know So if I want
to do that and if my size off
my images one makeup it's what should be
my size off imports given toe fully collector
neural and somebody do the one megabits one
megapixel Perfect That means I need to have
a network We should have one Megan Neurons
doesn't input If one mayor neuron is my
import what will be my hidden layers Can
I say I can say to maker Then
we reduced to one bigger than half of
me God and 25% of mega like that
And then we'll get an r potus correct
So we just saw in a simple newly
elected of which redesigned there were around 3000
parameters to be learned Now imagine we're having
one mega parameter just as input and remaining
We don't even consider it How many parameters
here to learn what's gonna happen here Each
e book will take hours and hours to
walk around and back Elite yes or no
Yeah What would happen either you need a
very powerful system for that hardware for that
orders You cannot do this So what we
do is we bring in a concept off
convolution Now what exactly CNN This is one
of the most challenging part off your course
here All right What is CNN is CNN
So let us say for example this is
one face that we have They're in the
image for example and our job is to
detect this face What CNN does is CNN
has instead of having rates If you people
remember in your letter looks right now we're
talking What brand the mice rates instead of
randomized rates We have randomized freed us This
field does What happens is they go and
get my deployed with this numbers it's nothing
But in matrix you people are really visualize
The image is nothing but a matrix consisting
off some numbers Each number is nothing but
a picture colored picks It goes and get
my deployed And from now on I will
use this as an important my fully connected
your it So look at the size off
this letter Say this is a three cross
three freedom So now I will import this
to my neural network I will input all
of these feature maps till I find out
one particular feature Matt which covers the whole
image more or the less this is what
it's seen On day I'm reducing the size
off my computation so that I could usefully
connected me relate back to do my image
for us Yeah Um fine In u s
a question Yeah yeah yeah Okay so it
is the most complex for very very complex
It is going to be one of the
heaviest persons that he was gonna be But
it's really interesting boy because the argument is
to reduce the image to a very short
size and then take that thing for image
force rather than wasting time and taking each
one of them to image costs way Always
use three b three Yes eso There is
a formula Basically the formula C is ripped
off The image minus picked off the filter
lasts twice off party divided by strike This
is the formula So for others I'm really
sorry I don't want to confuse you Buy
If I want to define an output sites
that means the size which I have to
give here is a fully connected new electoral
I need to have to decide some size
of my finger So then you have decided
this What do you do Issue keep take
does the side and take your f other
site And finally whatever answer you get it
will be your finger sex as I say
usually Okay Usually what we do is we
use really why we use a little because
it gels in with almost all of the
numbers similar to that three Cross three for
cross for and do cross through These are
the common numbers Will you should go in
general But almost all of the seeing sort
of tree Crossley If I said 40 cross
30 what's gonna happen I'm going to date
a very big feature again I'm going to
go in populate my fully connected new The
whole purpose here is to take smaller chance
to identify Let's Okay All right So who
for example for for for people who are
new to this just taken example What if
I show you something like this Save I
don't show the whole image I just show
you this Can somebody predict what exactly This
could be if I completed e or fish
So you need the whole image Toby predicted
A certain chunk off an image can be
taken An image A production will be thrown
out saying that it isn't for example recently
have been Let's just say that is our
dog So what we do is when convolution
runs on to the ears of the dogs
they're very few dogs were exactly Flatley's For
example it could be Doberman It could be
husky It could be say a German ship
if we only had learned So what if
I could just identify the whole dog rather
than standing the whole dog Just scan sudden
part of her tonight in the fight we
see a lot of competition time This is
what it's seeing another but I got that
But Mr Sorry I am deviating from the
topic but the total holding made I would
I would be no better That's a year
off a dog or a year off from
Robert B Nico Some of the condition it
workers to see the whole image rate Are
you going to the future that's in Doesn't
it Doesn't it does my only point over
here is rather than taking the whole imagine
throwing inside the fully connected years Look what
I do is I take some chunks out
off each one truthfully connected so the competition
time will be faster Okay Okay Yes So
this is what you're seeing All right Good
So this is what is your urine A
Glock I'll introduce your ways to this image
processing Not completely I don't want to burden
anyways right away but will take one example
and we try to bring some new network
and do some situation using that So let's
talk about the very first topic now This
is a big challenge in our industry currently
Now why do I say that really simple
example Um have you guys heard about an
LP I think Yes I've shown you some
improvement This Yes Also you guys have heard
about computer vision All right so what were
basically these topics are is are we can
also call the voice processing monsoonal And what
are these Stoppings is You have got a
pre trained data That means if you're talking
or nlb you have God Let us say
if you're talking about within and will be
You're talking about some kind off sentiment Analysis
What customers Talking about Easy talking Positive Negative
Good Bad about us if you want Oh
huge If you want to do this kind
of processing and in bulk you can use
an LP sentiment analysis here Now do this
What you need is you need a very
good corpus fight back and file off Yeah
So why do I say we need a
good back Corpus file Is we need to
have all the possible combinations here itself so
that I can train my newer network All
right this files are usually very heavy Um
if I Sure you lays an example offer
it If you look at this this is
a corpus file available with me for flowers
If you have If I want to go
if you give me an image and if
I want to classify that imagine one of
these flowers I need these kind of images
is a backup to train my model saying
night this is a killer here for example
Oh are this could be a deal So
there could be some positive examples That could
be some negative examples We don't know about
So this is the first challenge that we
usually face how to get these kind off
corpus is So if you Dr Martin voice
processing um I'm not sure guys ever shown
you any time Anything on the voice ever
No no Right This last week you said
something in the perfect type two right here
Perfect So that voice processing imagine how much
level off voices it had to record to
understand So that corporate that we had was
from Google For now it is free So
we are using it Google was kind enough
to gather certain people from certain ethnicities sort
in regions so speaks or in time of
boards so that whenever I speak something he
applies to match my accent and my world
with the nearest value in price To predict
that I said that if you observe last
week whatever I say it not 100 person
was a match but yes majorly understood what
I was trying to sick So the complete
challenge for any neural network here lies to
gather this kind of combinations so that we
can play in a network That is what
we call this all a mendacious Okay you
can relate that topic with augmentation Now what
do I mean by a recommendation Let us
say you have given this as an image
to your new network saying that this is
a cat Now what if I tell my
image Or what if I rotate my image
Or what if I crop my image What
if I zoom in my whatever you do
with this If in case if you train
and urinated upon though this is only one
picture saying this card and if you present
this to him a simple unit or getting
not taking Why Because if you observe the
pixels president here and if you take the
same locations in your image currently made that
might be empty space So obviously it's going
not going to match it So it's very
important for us to make sure that before
we give a date I toe in your
network We have been a proper augmentation documentation
Indians We have been this kind off changes
Now it is in our hand whether we
have to do this So there are certain
functions available within your networks which will allow
us to do augmentation so you don't ask
us if you want to rotate If you're
on the flavor if you want to crop
should resume whatever you wanted All right Sometimes
even it allows us to change the color
source So this is quite data or commendations
or if I just prison this sort of
major topic But yes Aziz much talked about
because not all of us have very with
access to all the sticks Right So please
remember it says we may not have a
big data set to create more data So
how do you create the more data you
can create more redeye using your argumentation techniques
like this All right so just take an
example often argumentation What we can do is
this is an image which belongs to a
dog if you want Oh have or is
a ransom the image let us say from
color here's times on into a gray scale
From there I'm giving it to see it
And now in this case it is easy
for us to identify What if tomorrow the
snow and stuff is not only this much
faces are level We're not sure how comfortable
your noodle adorable baiting somebody's cameras on this
Yeah How comfortable the new letter will be
Right defender It's a husky right So in
those cases augmentation will be off a very
good help So this is one case where
you can see I need organization and definitely
in computer vision You need a good backup
of data Otherwise you run and dogs There's
no use Okay What are we can't go
inside Augmentation So you can do flex You
can do rotations Cramping scaling color jitter means
changing the you and all the spot effort
Other 3 80 of techniques could be If
we talk about convolution it's it could be
translation rotation stretching sharing lens distortion so you
can do a lot of things Now you
might be wondering what are these terms This
will come in the first week off competition
where we will use certain Fritos on top
of an image such a way that will
change the look and feel of the age
So this is something like even we can
save on Instagram You know those instant freighters
are there Yeah some of the food doesn't
know the chain the background the blowback running
So this is what the Ferguson so you
just have to define offender which could be
multiplied with an Imagine You'll get a new
image out of it That's so This is
what is by lens distortion Share So this
is a recommendation Any questions Pretty straightforward So
in Tampa Division will see the real application
offered for our new networks Part of it
we usually have a good backup date I
should not be in big trouble All right
now coming Toe rates initialization Now this was
a common anything In the first session a
lot of people asked Do we actually put
randomized weights I will say it by default
We do it But if we don't want
to do it there are certain different weights
available with us Let's see What are those
weights So one Yeah So this is the
total rates that begin Sebi harridan in our
hands Either we can use zero initialization We
can put all the weight Zero Yeah we
can put random my random initialization That is
what we do Currently We have something cause
the area initialization We have something called ECI
and there are many more So if if
you are a researcher in a company and
all If you want to go you can
even develop your own Bates sent right now
in today s case study that I'm gonna
show you We will see Random Worse is
achy So first reels we will start with
80 will implement a model and then we
will change it too random and we'll check
how the accuracy changes All right so these
are the two common ones If you want
to use it else comes your zero initialization
So any idea what will happen if this
is you So any idea where I can
use zeroing nationalization Because you remember What is
your initialization Your directly putting rate Zero So
no matter what your inputs are how good
is going to be zero on me getting
my point So this is one of this
This is a peculiar um example off Wait
So here they're showing what if what happens
when w is equal to zero So the
weights are zero Definitely Whatever is your import
that I say all of them are zeros
The same thing they're gonna get is an
output to be very frank But remember inside
this we were talking about not only your
input weight gets my deployed with input gets
multiplied by way But also it gets added
by a bypassing factor Remember that So sometimes
if you want to use a strong biasing
factor what you know wait zero you when
we can do that aren't the only issue
is the output will be very diminished Basically
because the company neural network will be running
on by us is short us Basically it
was resist Everything is here So if I
choose a very good Activision functions they like
and just sigmoid where there are chances that
anything which is zero residency also has coped
a bus plus a biasing factor There could
be chances that yes you might get some
outputs I will not say it's a very
good way to do it But in worst
case and around certain situations we don't know
In case you need that even you can
go ahead and initialized zero All right But
But but But it is not the first
The first time would be zero but the
basic actually coming from the second iPAQ changed
right would not add me to the Yes
The things will change from 2nd April correct
That's why I say the biasing factor will
be the ruling Ah ruling thing will be
so there are chances that from the second
point of third point you might Then we
might start changing the rates itself So if
you want to start with a very fresh
you know you don't want any junk in
your network You can do this but it
will take some time It will take more
He talks to come to some some some
normal number I hope you getting my point
Yeah Perfect So this is what it is
on Duh majorly These are the ones we
use it apart from that random nhe other
the common ones So today we'll see both
of them Okay Have given some some text
Toronto that you guys like it I published
the PPT You could just go through the
same thing What we explained on if you
want to learn what is H e Basically
with the formula forage so divided by size
off L minus one and I'll give you
the company details of that You don't have
to get into the detail amazingly because ah
we use always so far in my career
I've never used Etienne's area To be very
frank could be used in a very tight
constraint on Normandy condition where you have you
don't want the perimeters to go certain beyond
certain characters expire values In that case you
can use this to else random salts majorly
the job Okay so this was your rates
so we can use any of them That's
what I meant Overview Moving on Coming toe
the topic off regularization What do you mean
by regularization In machine learning You remember we
did something on regularization like standard scaling our
clipping If their data goes beyond certain value
like outline treatment If you will remember something
like that could be done Wife See you
have a new lead work on and uh
you have done are randomised weight kind of
thing So when you're given input say you
have given really was an output So in
all of these layers Ray Lewis in So
what happens is when your input gets my
deployed with certain weights and goes to the
next layer there are chances that you might
get a magnified image or magnified signal of
feel confident about their chances Depends on our
big we don't know Also when it goes
here there are chances that you might get
a magnified Western again Yeah Anyway it's gonna
follow a low activation function itself I agree
But let us save my input of very
low And because off waits it has got
magnified So because of this issue what we
do is we try to introduce one extra
layer in middle of all of these That
is what we call batch normalization All I
will say standards killer for a neural Linda
so that they never and output comes out
over network It has to pass through This
gets normalized and then goes to the next
one so that we have a check that
whatever data were giving isn't bombs It's not
going out of your box That's sometimes it
is useful Sometimes it is not So Tomorrow
when you're doing in your network and you
are having an issue of over footing or
under fitting those times you can go back
do this kind of feature engineer It's not
necessary to put for all of them You
can start with one or two and then
if you see a postures will start putting
for all of you will see today in
our case study How do you do So
this is quite batch normalization Second concept of
regularization is drop out Drop out means that
we questioned all of you Now as I
say there is no formula for neural networks
as we say that there is no formula
for out input hidden less There's no formula
for total number of neurons within him way
have not no clue over Do you think
all the layers and all the neurons are
duly important for driving out No no right
No Right So it could be like the
conduct we can say like that There are
five employees in a company Three off them
are driving the project to off them Sometimes
they come and helps on time to do
so even if their presence and no presents
Also not going to affect much definitely by
removing them efficiency Good condom But the border
on these days including report only for certain
point After that we normalize Same is the
case here Not all the weights are not
all the neurons that we have actually put
So what we do is we bring in
a concept off trouble Where if I said
drop out is equal 2.2 that means 20%
off the neurons Please manually switch them off
no matter Waters randomly choose 20% off them
and switch it off Yeah this could be
used as an accuracy increase our modern tuning
part of it at the end again You
if you ask me how 20% I got
random by There is no formula for that
start with 10% 20% 30% Some of the
implementations I've seen people going up to 60%
70% And all this I feel is not
a good thing Why Because the only the
only created this And now we're saying 74
points 70% of the network is digital's So
it zey useless hardware for us We're keeping
it in its of no use So rather
than doing 70 16 or what you do
is you manually go in shooting them No
problem But I usually keep myself up to
50% or more than it's not fair from
on site Really Frank So you when you've
been do drops like this where you been
as the name dropper slash it off This
is a drop or concept make No Um
so So this is what it looks like
So here on the left inside they're showing
a fully connected new in it Here they
have reduced shortens neurons are offered and model
gives almost a similar performance Looks like instead
of yes in this case brought aboard kind
off What's all right No Okay So if
you look at this I feel this is
a little silly example but yes you create
some impact What it sees is what if
you have got some features So this particular
neuron classifies that a particular image that we
have given has an ear It has a
tail It is funny It s closet doesn't
having mysterious looks So if you want to
predict this as a cap has having an
ear is not going to make a very
big difference To answer was a lot of
animals will have that having close makes a
difference I will say about this particular raided
This should be given more So there are
certain weights and neurons which could be switched
off And even if your door you're going
to end up with some classifications not ever
increasing So that's just an example Don't show
this This is your drop out Another interpretation
off a drop pod Could be um it
is it Is it kind off You know
simulated ensemble modern Every people they will go
get simulate this Understand what I'm trying to
say here drop out Is training a large
ensemble model Do you agree with that Oh
no Uh not able to connect on this
Monday You remember what his ensemble techniques in
recently Probably t four models back You would
have that this What do you mean They're
ensemble We have got leaked classified IRS which
are nothing but your individual Al Gorgons What
we do we combined them into one platform
and then we take let us say I
given input through all of that and the
input that I've given Actually the target column
is deal Let us say the first I'm
gonna them classifies it as one next 10
next 10 Next one is that means I
will take the majority order for all three
of them And I would say yes My
final answer is Ego said this is called
a week Classify This is called a stronger
glasses Agreed This is our ensemble technique So
if you remember we're done random for us
bagging boosting all those tickets Same thing What
if I keep Because every time I remember
I said every time when you read on
your model 20 personals randomly chosen values get
switched on and switched off So can I
say in verse first cycle these neurons will
be active second cycle It is possible that
this neuron could be active in this need
on video you don't know So sometimes in
a possible scare you can think this as
a ensemble technique Where you by mixing and
matching various Dorantes you are creating a final
lots Justin analog you don't have to worry
about it We're not going to let Madonna
ensemble here just to give you a visualization
off folks All right this is so so
the challenge would be the training How hold
the training the every set of training would
be on on on a set The words
on would it be random within and among
the parking one drink in one he book
seem them in money Book one set off
neurons will be dropped in the next people
There are chances not the same one gets
back to because as I say it is
randomly chosen It is kind of costly sometimes
for us because in one case in the
last Depo Calado said This was switched us
that this that this particular thing was switched
off So the raid was not improvised onto
this So if I switch it on in
the new equal again I have to retrain
his week That's why I say it's a
little costly affair to do this so no
one will do drop worst case when the
model is not improving at all whether I
do or some problems and even sometimes after
pushing the reports also model is not going
to increase because off this kind of issue
as I said just a backup technique available
with us That's and I totally feel it
is a waste off hardware because he fired
If I have said that I need these
neutrons and if I switch them off I
don't know it's not fair really from Yeah
so I think gradually this is art This
is what you can do Toe junior model
and apart from this for during your modern
you can use different type of e parks
different bad size different activation functions Ah say
different output functions different optimizes whatever we sort
today you can mix match them and change
your And if you do mix match and
trying to get it right Then of course
is just the best form of mixing Majesty
A great and random Seen it and thats
gonna be That's very expensive So it is
mulling a brute force for good in tandem
So we'll do one thing image in this
case is I'll introduce you guys to collapse
I don't know if some of you already
started using Columbus Guard Aram started last I
mean really did a collab in last recommendation
engine because the the size of so big
that the all admissions and addressed really last
last project Almost everybody didn't call So you
guys are good at it So you if
you want to do great search no do
it in collapse At least it will not
be so bad as a company At least
the outward would be faster to be frank
But yes Did such run things like I
will actually win Coolum for all of us
Class Don't know 100 Some of the training's
OK Way grand out of memory And we
got an order Memory who When we had
the constraints Ah a lot of parameters of
Eugene Okay Okay Good clients Alexey But from
my side How By dealers I go by
gut feeling and mixed match So this is
Ah little time taking and home implementation That's
why people sometimes reframe from using it Okay
good So I think we have done so
Any business problem Any business Christianity business problem
that we use um be planning for Or
is it primarily picture video Audio Y senator
t You know that type of their business
problems Also that we tend to use I
know that the example also said that you
can use any recognition problems in seven mission
when you think is deep learning but running
invalid Your business case that you use it
for yeah so immediately you use it for
systems we should dynamically changing first Awful Why
let us say you have got a customer
for example I'll give you We have a
customer Airbus for example Yeah So Airbus usually
in Ah four minute or flight Never self
Airbus generates around three db of data for
example I'm not sure Say please TV off
the paginated for flight or minutes Not even
seconds So what happens You have brain animal
Yeah Now just imagine if you wait for
safe one year How much amount off New
data your airways congenitally or your company engine
So what's gonna happen The data that you
have more than your brain on if you
take the difference with the newer data And
if you find it a significant difference onto
that what's gonna happen This Emily that you
have designed is normal Valid A greedy Yeah
So what You have to do you to
retire it Your retrain on the nude It
it is not dynamic on Listen until you
have a system where every day it gets
retired it gets retained and deployed back If
you are a system like that very good
offer So in this kind of case studies
you can bring in your your little because
you're allowed rocket is the sea Whatever input
your guilt I will just myself and I'll
give You know even this has to recall
No doubt but this is much easier than
this because if you remember in ML you
have to do a lot of changes Your
roof utilization stew names correlations Lord of things
you're getting you to check and then you're
there is in this were free from all
the issues even we do not even go
into for normality Here you can see that
that level off sophistication could be accused by
this So this is one way to retrain
the data changes every day Which one You
are newly neural nets You don't need to
retrain it every day Is that it Is
The data is changing is what you're saying
Get leaders training helps us to retrain them
So we have certain So windy Gorbach going
when we cross the basic level of your
letter every shows waiting in itself there quite
still feeling a little real automatically retailers I
dread I reserved when expressing communication systems Yeah
yeah no In recommendation systems under collaborate differently
You had this one maintains your know how
often this data they just tell you People
know that you wait because that based collaborative
separate switch from might be from one group
to another depending on his choice and current
values So if Amazon says that I will
keep a gap of 15 days you know
to sync up my date to refresh made
it What's gonna happen for a period of
14 days like Amazon might be sending the
wrong recommendations to this custom because this custom
has already moved out of the group So
what does this uh amazon has to do
Amazon would be having a board which will
trigger this letter say every second or every
10 seconds so that whatever recommendations are thrown
to a customer are not vestige We'll see
when If you if you get 11 recommendations
No you lose interest are simple So there
is a business behavior problem and also function
behavior problem because same thing we do it
over here We have got some triggers which
will tell the neural network to retrain it
steps on the new lead These are called
self Healing Your necks The best example I
will give you is um I will say
Google car for example So when the Google
guards on the road so that that's the
topic of reinforcement learning anyway it's a different
domain again But yes what they do is
they would be having a certain triggers So
say every 15 20 seconds I have a
system to identify Faces are different obstacles on
the road Retrain myself because we will Does
not know what new object is going to
come and sit over there All right So
if if a car is not able to
identify it what's gonna happen is going to
be my crash Or it might stop which
might stop that This So I would say
it's a very different level But yes this
is what is currently going on in the
market So you would have heard about according
boards Have you guys heard about this I'm
gonna be working on one of them You
just tell you Just write it down Got
of them The bottom called for you So
let us see You wrote something Which board
is not able to get it What did
you do It will take it up Could
retrain incense and then give a solution back
Please You just have to scribble You know
what you see are recorded up for you
You mean like this fight on good also
you being back where Yeah Any technology independent
Okay technology independent So I waas planning for
an industry session under this yet I'm not
to complete this To be very frank the
basic working sting is with me But the
only issue is the car Because that I
have because I need to have a carcass
with the letter Say if I say heart
in court my video So when I say
this my board will understand what is heart
What is in Cordant Tried searching my car
calls It would pick up one off my
ah uh from that this is okay Okay
meters I need this back and I need
somebody who makes and listen really like going
editor It's on with the work Currently I'm
struggling with this Okay on by a whole
host is also like some people are coming
with this You need this automated testing right
That little they're using our tradition incidents have
got any idea under Yes Yes I have
deployed one True Yeah Uh huh Okay Lets
him I'm being a website I won't automate
it using aims What do you do Actually
yes Let me show you because I hail
from the background over tradition as a full
stack developer to data finds So we'll have
a system here I will give you water
systems See what is to happiness Um there
is a court to see So this is
my release number one So that let us
say in in this particular product that are
leaves one released wanted Okay Early Amel Released
one And depending on my features that I
have released in the stuff I have done
some manuals I have a manual disturbing Aaron
Automation Just about a business ISS So Emanuel
I will say I can have somebody on
performance testing on functional testing and I will
see on security Yes yes yes On automation
die electricity Talk about selenium and UFT The
most famous once Okay this little states of
it So selenium is connected to that And
this depending on the web See just the
festive itis stripped and their script ready which
would be run onto my release number one
So let us say takes I don't next
days off time to do nice comeback with
release number two Now in this release number
is there a possibility is that the line
would have changed certain requirements And these guys
have changed certain patches here and there Yes
if I if I want to read on
the script it will not run I have
to make changes here Corrupt So again everything
Why amount off days to do that sort
of tenderness As soon as you you bringing
release to I will compare release to with
release one Oh find out places where the
changes that Yeah And you're going to the
court You mean in the court Even find
places where there a chance only do that
You'll have the scoring Okay Yeah We used
to be for that We use and will
be so in any We will try to
summarize what exactly that court does But to
do that you need a very good corpus
So if I write a court for you
notice if I write a for loop for
you okay Could have good amount of four
loops with me so that my could understand
that Okay A for loop Does this job
Yeah So it is a very difficult task
to get that corporate but once you have
the corpus in Khandala is very simple So
now what I do is I try to
find out the difference buster for okay I
say let us say my threshold value If
if If the automation justice is that if
the changes is around 10% off the script
it's OK I will do You don't have
to a costly affair like this one say
my quarters chained by 50% before exams It's
what I lose that pinpointed changes and using
what you see are aggressively matrix I will
say if this has changed a particular portion
off My script also needs to be changed
So I will go and kill the point
us for the automation Distrito Gwen teach That's
okay is the stuff I agree But now
he will know where to go in recent
So that subversion number one that we deploy
for automation testing But how we are connecting
that good with the script Taskings too Until
they have a tool We have tools for
that So we call it you track and
that this is no sales to trying on
and even Gina allows you to do traceability
Doesn't Yeah there is something off IBM rational
If I'm not wrong that does IBM does
There are so many doors which does business
analysis anything so you can immediately use them
to find it Principally minutes That's so and
And also again is this big missing But
what there are there are some tools which
according to its right is doing developmental looking
for tools like optimal trays Ah computer associates
CRB and China requirement designers another two And
if you do that is a body Do
there is You're being kind off space suspects
the whole requirements like a business process dagger
and that kind off that kind of automates
artists And wait Anything that happened in the
court you go back to the visual designer
changing requirements and then a lot of medical
gender But look for these two is not
very cheap tools but but But they are
pretty popular OK OK sure So as did
a little says they could Yeah but the
corner court generator you know I've uh uh
I've I've seen quite a few startups in
coordination is one very published out of in
Canada last year and gained a lot of
traction Is one in barriers that I try
to look for them but they I have
been very stump generation scaffolding generation right They
just couldn't go beyond that That's what I
felt right on And of course today you
are looking at it I mean we should
also look at the all the O'Quinn sources
and get Twitter's publicly available ghetto and trying
to train models based on all get tangles
Most of the um one of a cyber
uh ethical hackers do that right And you
go look it'll the court and find her
where there is a look So what I
did waas unofficially rated waas Whatever internal tools
we have in my organization we collected all
the cords with good comments you know with
functional Commons Agency that what does the below
patch do where it starts very inch If
you can get hold off very rich corpus
like this then designing this in an MP
is not a big job The only issue
which I found waas Sometimes the court becomes
very generous to be very franks If you
have a very high fight customize cords that
will not work here the board will will
will produce very generous stuff But yes down
the line we are evolving And also I'm
expecting some of these larger companies like Google
and Amazon to share some charity Corpus is
with us We can go ahead with this
you know in a public forum Otherwise what's
gonna happen Every company will have this confidential
spots We say I cannot share it with
us Why It was accords are in the
so you'll still rape But this is going
to be the next big thing where we
will replace the quarters itself So I can
fire a virtual machines injectors which will be
eras my according guys and let them develop
Yes The only issue is where you need
level A little bit of customization That's really
human enough Interference will not come into this
topic What we don't know the next was
in this Or was your number one the
next words in one word it was we
collected all the changes that daughter mission person
did you know as a corpus again So
we give this back to our date of
its ok next model What we did was
even we were commonly that if this weather
changes form you can put this script But
that was possible before one particular for Actually
Norman That means if you have God a
project it is what similar releases It is
possible only go there because of the business
logic problem So let us say if we're
talking about most of these bends so for
all the topics are selected Mercedes Benz this
model could be trending up But if you
bring in some other account it will not
because the business logic over there is the
full there was Sometimes we have a sippy
and all flying around in little you know
So when it becomes in you know you
guys if you buy the world on a
lot of business logics routine going here up
in In those cases the single failed but
yes in a similar environment it can give
a very good recommendation So as far as
I know Vyas about off I showed a
very good at all Why on this especially
on building off commissioned vessels it's even got
a very good uh number Yes we need
some manual approaches here and there But yes
it was predicting very well once it was
But now what's happening is this was a
opens like the first developed from my people
toots Tomorrow if one of them collapses the
whole system will collapse So the bigger companies
like HB try Sanders s a p All
these are coming up with their own Now
more so Like if you talk about a
city that is something Well solution Manager issue
buys a world apart Now there are ANALITICO
options given insight solution man Workflow workbench them
Uh what is a tied for options on
this We just crypt it You will get
the logical the whole whole logical So these
things are now replacing these kind of monks
They are packaging it up making it universal
and selling it The soon you and you
might find that our packages inside UFC which
do this So there are some news under
that I want you I want you least
Yeah maybe I need Also I'm just finding
back with Stark when we're very local Do
one thing You connect with me offline I
will have to start to suffer I can
teach you something but yes I can give
you some basic corpus is Or is it
because we can refer to some of the
deep chords down ordered committed and using justice
This okay good Yeah So So So what
I'm trying to see now is the thing
that is going to happen after your this
model That is your discomfort division on NLB
That will be off different level Basic So
these all other applications are outcomes off these
more models I mean anything Yeah So again
I will say uh Emily was good but
this is what is the next future So
some of you who are very senior over
here in your organizations having an idea under
this you can do a lot of proposals
to her clients You can do a lot
of cross selling Basic This brings us to
the end of this tutorial on convolution neural
networks Now before you guys sign off I
like to inform you guys that we have
launched a completely three black form called as
Great Learning Academy We have access to free
courses such as the I cloud and they
still marketing You can check out the Lincoln
description below So guys thank you very much
for giving the session and have agreed learning
