AI vs Machine Learning vs Deep Learning | Machine Learning Training with Python
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.
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