Hello, everyone. This is Atul from Edureka and welcome to today's topic
of discussion on AI vs Machine Learning
vs Deep Learning. These are the term
which have confused a lot of people and if you
too are one among them, let me resolve it for you. Well artificial intelligence
is a broader umbrella under which machine learning and deep learning come you
can also see in the diagram that even deep learning is
a subset of machine learning so you can say that all three of them
the AI the machine learning and deep learning are just
the subset of each other. So let's move on and understand how exactly the differ
from each other. So let's start
with artificial intelligence. The term artificial intelligence was first coined
in the year 1956. The concept is pretty old, but it has gained
its popularity recently. But why well, the reason is earlier we had
very small amount of data the data we had Was not enough
to predict the accurate result, but now there's a tremendous
increase in the amount of data statistics suggest that by 2020 the accumulated
volume of data will increase from 4.4 zettabyte stew
roughly around 44 zettabytes or 44 trillion GBs of data along with such
enormous amount of data. Now, we have more
advanced algorithm and high-end computing
power and storage that can deal with such large
amount of data as a result. It is expected that 70% of Enterprise
will Implement ai over the next 12 months which is up from 40 percent
in 2016 and 51 percent in 2017. Just for your understanding
what does AI well, it's nothing but a technique that enables the machine to act
like humans by replicating the behavior and nature
with AI it is possible for machine to learn
from the experience. The machines are just
the responses based on new input there
by performing human-like tasks. Artificial intelligence can
be trained to accomplish specific tasks by processing
large amount of data and recognizing pattern in them. You can consider that building an artificial
intelligence is like Building a Church, the first church
took generations to finish. So most of the workers were working in it never saw
the final outcome those working on it took pride
in their craft building bricks and chiseling stone that was going to be placed
into the great structure. So as AI researchers, we should think of ourselves
as humble brick makers whose job is to study how to build components
example Parts is planners or learning algorithm
or accept anything that someday someone
and somewhere will integrate into the intelligent systems
some of the examples of artificial intelligence
from our day-to-day life our Apple series just playing
computer Tesla self-driving car and many more these examples
are based on deep learning and natural language processing. Well, this was about what is AI
and how it gains its hype. So moving on ahead. Let's discuss about machine
learning and see what it is and white pros of an introduced. Well Machine learning came into existence in the late 80s
and the early 90s, but what were the issues
with the people which made the machine learning
come into existence? Let us discuss them one by one
in the field of Statistics. The problem was how to efficiently train
large complex model in the field of computer science
and artificial intelligence. The problem was how to train
more robust version of AI system while in the case of Neuroscience problem
faced by the researchers was how to design operation
model of the brain. So these are some of the issues which had the largest influence
and led to the existence of the machine learning. Now this machine learning
shifted its focus from the symbolic approaches. It had inherited
from the AI and move towards the methods and model. It had borrowed from statistics
and probability Theory. So let's proceed and see what exactly is
machine learning. Well Machine learning
is a subset of AI which The computer to act and make data-driven decisions
to carry out a certain task. These programs are algorithms
are designed in a way that they can learn
and improve over time when exposed to new data. Let's see an example
of machine learning. Let's say you want
to create a system which tells the expected weight
of a person based on its side. The first thing you do
is you collect the data. Let's see there is how your data looks
like now each point on the graph represent
one data point to start with we can draw a simple line to predict the weight
based on the height. For example, a simple line W equal x minus hundred
where W is waiting kgs and edges hide and centimeter this line can
help us to make the prediction. Our main goal is
to reduce the difference between the estimated value
and the actual value. So in order to achieve it we
try to draw a straight line that fits through all
these different points and minimize the error. So our main goal is
to minimize the error and make them as small as
possible decreasing the error or the difference
between In the actual value and estimated value
increases the performance of the model further
on the more data points. We collect the better. Our model will become we can also improve our model
by adding more variables and creating different
production lines for them. Once the line is created. So from the next time
if we feed a new data, for example height
of a person to the model, it would easily predict the data
for you and it will tell you what has predicted
weight could be. I hope you got
a clear understanding of machine learning. So moving on ahead. Let's learn about deep learning. Now what is deep learning? You can consider deep learning
model as a rocket engine and its fuel is
its huge amount of data that we feed to
these algorithms the concept of deep learning is not new, but recently it's hype as increase and deep learning
is getting more attention. This field is a particular kind
of machine learning that is inspired by the functionality of
our brain cells called neurons which led to the concept
of artificial neural network. It simply takes the data connection between all
the artificial neurons and adjust them according
to the data pattern more neurons are added at the size of the data is large
it automatically features learning at multiple
levels of abstraction. Thereby allowing a system to learn complex function
mapping without depending on any specific algorithm. You know, what no one actually
knows what happens inside a neural network
and why it works so well, so currently you can call
it as a black box. Let us discuss some
of the example of deep learning and understand it
in a better way. Let me start with a simple
example and explain you how things happen
at a conceptual level. Let us try and understand how you recognize a square
from other shapes. The first thing
you do is you check whether there are four lines
associated with a figure or not simple concept, right? If yes, we further check if they are connected
and closed again a few years. We finally check whether it is perpendicular
and all its sides are equal, correct, if Fulfills. Yes, it is a square. Well, it is nothing but
a nested hierarchy of Concepts what we did here we
took a complex task of identifying a square and this case and broken
into simpler tasks. Now this deep learning
also does the same thing but at a larger scale, let's take an example
of machine which recognizes the animal the task
of the machine is to recognize whether the given image is
of a cat or a dog. What if we were asked to resolve
the same issue using the concept of machine learning
what we would do first. We would Define
the features such as check whether the animal has
whiskers are not a check if the animal has pointed ears or not or whether its tail
is straight or curved in short. We will Define
the facial features and let the system identify which
features are more important in classifying a
particular animal now when it comes to deep learning
it takes this to one step ahead deep learning automatically
finds out the feature which are most important
for classification compare into machine learning where we Had to manually give
out that features by now. I guess you have understood that AI is a bigger picture
and machine learning and deep learning or it's apart. So let's move on and focus our discussion
on machine learning and deep learning the easiest
way to understand the difference between the machine learning
and deep learning is to know that deep learning is machine
learning more specifically. It is the next evolution
of machine learning. Let's take few
important parameter and compare machine learning
with deep learning. So starting with
data dependencies, the most important difference
between deep learning and machine learning is
its performance as the volume of the data gets increased
from the below graph. You can see that
when the size of the data is small deep learning algorithm
doesn't perform that well, but why well, this is because deep
learning algorithm needs a large amount of data
to understand it perfectly on the other hand
the machine learning algorithm can easily work
with smaller data set fine. Next comes the hardware
dependencies deep learning. Are heavily dependent
on high-end machines while the machine learning algorithm can work
on low and machines as well. This is because the requirement of deep learning
algorithm include gpus which is an integral part of its working the Deep learning
algorithm requires gpus as they do a large amount of matrix
multiplication operations, and these operations can only be efficiently
optimized using a GPU as it is built for this purpose. Only our third parameter will be feature engineering well
feature engineering is a process of putting the domain knowledge
to reduce the complexity of the data and make patterns more visible
to learning algorithms. This process is difficult
and expensive in terms of time and expertise in case
of machine learning. Most of the features are needed
to be identified by an expert and then hand coded
as per the domain and the data type. For example, the features can be a pixel value shapes
texture position orientation or anything fine the Performance
of most of the machine learning algorithm depends on how accurately
the features are identified and extracted whereas in case
of deep learning algorithms it try to learn high
level features from the data. This is a very distinctive part
of deep learning which makes it way ahead of traditional machine learning
deep learning reduces the task of developing new feature
extractor for every problem like in the case of CN n algorithm it first try
to learn the low-level features of the image such as
edges and lines and then it proceeds
to the parts of faces of people and then finally to
the high-level representation of the face. I hope that things
are getting clearer to you. So let's move on ahead and see
the next parameter. So our next parameter is
problem solving approach when we are solving a problem using traditional
machine learning algorithm. It is generally recommended that we first break
down the problem into different sub parts
solve them individually and then finally combine them
to get the desired result. This is how the machine learning
algorithm handles the L'm on the other hand
the Deep learning algorithm solves the problem
from end to end. Let's take an example
to understand this suppose. You have a task
of multiple object detection. And your task is to identify. What is the object and where it
is present in the image. So, let's see and compare. How will you tackle this issue using the concept
of machine learning and deep learning starting
with machine learning in a typical machine
learning approach. You would first divide the problem into two step
first object detection and then object recognization. First of all, you would use a bounding
box detection algorithm like grab cut for example
to scan through the image and find out all
the possible objects. Now, once the objects are recognized you would use
object recognization algorithm like svm with hog
to recognize relevant objects. Now, finally, when you combine the result
you would be able to identify. What is the object and where it is present
in the image on the other hand in deep learning approach you
would do Process from end to end for example in a euro net which is a type
of deep learning algorithm. You would pass an image
and it would give out the location along
with the name of the object. Now, let's move on to our fifth comparison
parameter its execution time. Usually a deep learning
algorithm takes a long time to train this is because there's so many parameter in
a deep learning algorithm that makes the training longer
than usual the training might even last for two weeks
or more than that. If you are training
completely from the scratch, whereas in the case of machine
learning it relatively takes much less time to train ranging
from a few weeks to few Arts. Now, the execution time
is completely reversed when it comes to the testing
of data during testing the Deep learning algorithm
takes much less time to run. Whereas if you compare it
with a KNN algorithm, which is a type of machine learning algorithm the test
time increases as the size of the data increase last but not the least we
have interpretability as a factor for comparison
of machine learning and Running this fact
is the main reason why deep learning is still
thought ten times before anyone uses
it in the industry. Let's take an example suppose. We use deep learning to give automated scoring two essays
the performance it gives and scoring is quite excellent and is near
to the human performance, but there's an issue with it. It does not reveal white
has given that score indeed mathematically. It is possible to find out that which node of a deep
neural network were activated but we don't know what the neurons
are supposed to model and what these layers of neuron
we're doing collectively. So if able to interpret the result on the other
hand machine learning algorithm, like decision tree gives us
a crisp rule for void chose and watered chose. So it is particularly easy
to interpret the reasoning behind therefore the algorithms
like decision tree and linear or logistic regression are primarily used in
industry for interpretability. Before we end this session. Let me summarize things for you machine learning uses
algorithm to parse the data learn from the data and make informed decision based
on what it has learned fine. Now this deep learning
structures algorithms in layers to create
artificial neural network that can learn and make Intelligent Decisions
on their own finally deep learning is a subfield
of machine learning while both fall
under the broad category of artificial intelligence
deep learning is usually what's behind
the most human-like artificial intelligence. Well, this was all for today's discussion
in case you have any doubt feel free to add your query
to the comment section. Thank you. I hope you have enjoyed
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