welcome to Intellipaat guys and today I'm
going to discuss about one of the
hottest topics which are everywhere
nowadays and these are artificial
intelligence machine learning and deep
learning. I bet you guys that this topic
is surely going to captivate your mind
so let's begin. First of all let me
quickly go through the agenda of today's
video so first of all we are going to
look at the basic difference between
these terms whether they are correlated
or different and then what exactly are
artificial intelligence, machine learning
and deep learning. Then we'll go a little
deeper and understand how machine
learning works followed by the working
mechanism of deep learning and
immediately after this we are going to
discuss the key differences between the
two term and finally we will wrap up our
video by discussing the career growth
you can have in this particular field. So
let's begin what are these Are these somewhere
related to each other or are
completely different technologies
altogether. Let's explore. So first of all
let me show you guys one image and I'm
sure by looking at the image you'll get
a clearer picture about it so here it is
so you can clearly figure it out that
AI is a broader term under which
machine learning and deep learning fall
that is to say ml and dl are nothing but
the subsets of AI and you perform
artificial intelligence using ml and dl
technology. Let me also tell you guys
that there are many other kinds of
technologies available using which you
can perform artificial intelligence but
then these two technologies that is ml
and dl are the most impactful and
powerful one and that is why we are
going to discuss about these in this
video. So by now you must have got that
all these terms are co-related now let's
discuss what exactly are they so the
simplest definition of artificial
intelligence can be described as the
intelligence displayed by machines or in
other words the capacity of a machine to
imitate intelligent human behavior that
means AI is defined as the ability of a
computer program or machine
to think learn
and make decisions just like us. So this
is a broader definition of AI now let's
talk about machine learning and deep
learning a little deeper. So specifically machine
learning is nothing but a bunch of the
statistical algorithms and tools to
learn from the data or rather training
data so machine learning here is the
science of getting computers to act
without being explicitly programmed. On
the other hand deep learning is
connected with algorithms inspired by
the structure and function of the brain
exactly and we call it artificial neural
network so let me explain it to you guys
so here we construct a larger neural
networks and train them with data I mean
more and more data and hence their
performance continues to increase with
the passing time and then deep learning
is again different from machine learning
because in machine learning you can
perform but up to a certain level
however deep learnings are used to
perform complex artificial intelligence
and needs huge, really a huge amount of
data. Now let me take an example to give
a simple introduction to AI okay. So let
us first look at the human perspective
of driving a car. So to drive a car what
all actually do you need let's check it
out
so you need to have sensory functions
just like vision and sound so to watch
the road and the other vehicles on the
road and then you also need to have
cognitive functions like memory so we
make decisions like when to stop at a
red light or wait for a pedestrian to
cross the road so we are using our
memory to make these decisions and
finally we also need to have executable
capabilities to execute all these. right?
so this all actually form an experience
for us and that is the reason when we
drive a car for the very first time we
are just not very good at it but then
eventually we learn from the experiences
and get better isn't it so the same
thing happens in terms of the artificial
intelligence. They also I mean to say
machine also learn from their
experiences so in this the experience is
nothing but a large amount of training
data that the
machines are fit with in terms of
algorithms and hence we get the output
which in this case is self-driving car
so here again the machine would keep
learning from you know based its past
experience which is the huge amount of
data and hence we get the output. Now
let's go ahead let us now see how
exactly machine learning works so let's
say if we want our machine to recognize
that given data is a bird so in order to
achieve that we first need to feed our
system with the data of all kinds of
birds and then we also need to specify
the features of that particular object
which in this case our feathers,wings or
beak. Right? So in short we are kind of
forming patterns and want our system to
follow the same pattern and now after
all this when we give a test data
related to a bird our system would learn
from its past experiences and would tell
us that it is a bird and this is how
machine learning works so we take one
more example of the same let's say
YouTube recommendations of song. So based on your previous search history your
previous selection of songs it
recommends your videos. So this has been
possible because of machine learning and
this is how even in our daily life we
use you know products of machine
learning it just we don't realize it so
I'm going to be talking about those as
well in the slides to come. And now let's
talk about deep learning, how does it
work and how is it different from
machine learning. So in deep learning you
have got this which we call deep neural
network now why is it called deep
because it has got more than one hidden
layer we can simply check this out in
the image showing on the screen so here
you actually give input and then it
performs calculations and finally you
have got the output based on your
algorithm so let's say for example here
I am going to give the input of musical
instrument now unlike machine learning
we are not going to tell our machine
which path it needs to take or what
pattern it needs to follow so here the
deep neural itself would form its
patterns and
with the output and let me also tell you
guys that this deep neuron is also quite
as black box why so because it is very
difficult to know what exactly going
inside this to form patterns and that is
why it is called black box as well now
let's check out an example of it so this
is alphago a computer program based on
deep learning technology and it is
created by google's deepmind to play the
board game called go and at the right
hand of the image you see Lisa doll who
is the world champion for this game so
there was a competition organized
between these two that is between
alphago the machine and Lisa tall and
let me tell you guys that the result
might surprise you exactly
alphago actually defeated the world
champion Li but how could actually do
that so there's a reason behind it and
that is alphago's continuous
self-improvement yes it keeps learning
and improving every moment so a man
might play the same game let's say ten
times a day
but alphago is playing and learning from
the past experiences a million times a
day so it just does not take breaks or
it does not have days when it might not
feel like practicing and hence it
becomes nearly impossible to win over
alphago and this is why this depicts the
real power of deep learning so by now
you must have got that both the
technologies are used to perform
artificial intelligence now it is a sum
of the key features in between these
technologies so deep learning as I told
you guys is used to perform really
complex and intrigue calculations just
like in the case of alphago which is
four times as complex as the game of
chess at the other end machine learning
is used to perform simpler artificial
intelligence so something that you can
achieve using deep learning you can't
truly achieve using machine learning and
then the number second we have got the
amount of data so deep learning requires
a huge amount of training data like
truly huge to perform the calculations
whereas machine learning requires
relatively less amount
data and then to perform a ind planning
you need to have high-end machines as
they are used for complex intelligence
or calculations at the other end machine
learning you can simply work with
low-end machines as well and next comes
the performance level so in deep
learning you will be getting better
performance as human intervention is
minimized and most of the task is
performed by the machine itself whereas
in machine learning since humans only
decide the pattern and hence chances of
occurrence of errors are slightly higher
than the deep learning and make next one
the nest is debugging since deep
learning deals with complex data and
hence it becomes very difficult to find
out what exactly is going inside the
neural network and hence debugging
becomes difficult whereas debugging is
easier in machine learning since you
know what you have written and can
always go back and fix if something does
not go well and then the final one is
the amount of time that is taken to
train your machines so in case of deep
learning it requires a long training
time and on the other hand machine
learning requires shorter training time
so these were a few of the key
differences that we have got between
machine learning and deep learning so I
hope by now you have understood that the
artificial intelligence is an umbrella
term and this is performed using
multiple technologies but the most
powerful or impactful technologies are
machine learning and deep learning let
me finally give you some of the more
examples so that you can relate to what
we learned so far so let's start it off
with Google Maps an application that has
become a must for people living in big
cities so Google introduced machine
learning in Google Maps in 2017
improving the usability of the service
so these deep learning algorithms would
actually help the application extract
street names and house numbers from
photos taken by Street View cars and
then increase the accuracy of search
result now just imagine that with over
80 billion high-resolution photos
collected by Street View cars
analyzing these images by hand would
have simply been impossibly
time-consuming right and hence they're
machine learning takes its charge and
makes the life easier for you by giving
you this particular google map feature
and then we have got Google Search
divorce biggest search engine now offers
recommendations and suggestions based on
your previous user searches just like in
the case of YouTube recommendations so
now here as well is an algorithm called
ranked brain now through the use of an
intuitive neural network the concept
that I talked about you guys in deep
learning rank a brain identifies the
intent behind a user search and offers
them tailored information on that
particular topic and then we have got
Facebook so do you guys remember when
Facebook used to prompt you to tag your
friends so nowadays the social
networking algorithm recognize familiar
faces from your contact list and using
some seriously impressive technology it
gives you such kind of recommendations
which is again a part of machine
learning and then uber so machine
learning is a fundamental part of the
uber model
the tech giant actually uses this
algorithm to determine arrival times
pickup locations etc so whenever you
booked a cab uber aims to estimate its
arrival time as accurately as possible
so machine learning actually enables it
to do this by analyzing data from
millions of previous trips and applying
it to your specific situation so these
are a few examples of how artificial
intelligence has made her life much much
easier and simpler without us knowing
about it
and with this let us finally talk about
the career prospects of this domain so
you have got all these profiles and you
can simply make your career in any of
them based upon your interest and
ultimately you will be earning somewhere
around one hundred and fifty one
thousand dollars per year
completely exciting right and with this
we come to the end of this video where
we discuss what are a I M lndian and
also we discuss how machine learning and
deep learning works key differences
between
both and ultimately career growth into
this field so after watching this video
if you also feel excited about this
technology you can also learn it from
one of the best elearning platform and
that isn't an bad thank you so much for
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