In the previous lecture, we got an overview
of machine learning.
We have also seen what data sciences and how
it relates to fields of computer science mathematics
and machine learning.
So, here in this particular lecture, we are
going to go little bit further.
We have already understood in the previous
one, what are the different techniques for
machine learning, the broad classifications
of machine learning?
And how it could be used for IIoT scenarios?
And we have also seen a few examples of the
use of IIoT and machine learning combined
for addressing some machine some real life
problems in industrial settings we have already
seen that.
So, in this, we are going to continue further.
And first we are going to start with understanding
how machine learning compares with something
very also very popular nowadays which is known
as the deep learning.
So, how machine learning compares with deep
learning and how machine learning, deep learning;
they compared together with artificial intelligence.
So, this is this overall comparison.
So, this is AI versus machine learning versu,
deep learning.
So, holistically you know it is like this
that, deep learning you can think of is a
subset of machine learning.
And machine learning itself is a subset of
artificial intelligence.
So, finally, what we have is something like
this, you can think of deep learning to be
part of machine learning and machine learning
again part of artificial intelligence.
So, artificial intelligence, we have gone
through it in a previous lecture.
So, artificial intelligence talks about making
intelligent decisions.
Intelligent machines, which will make take
their own decisions without being explicitly
programmed to do so.
On the other hand, machine learning focuses
on learning automatically from certain object
features.
So, features may be present, may not be present.
In deep learning, for exam example this is
where you do not take help of any manually
identified features and automatically the
features are going to be found out on their
own right?
So, this is how this deep learning, machine
learning, and art artificial intelligence
is compared to one another.
And again, like I said in the previous lecture,
if you are interested to know more about deep
learning, there are courses you should basically
do semester long courses on deep learning,
semester long courses on machine learning,
and semester long courses on artificial intelligence.
This particular course is scoped only to give
you an overview of what is what, not beyond
that right.
So, that you feel yourself empowered and knowledgeable
in order to implement if required the different
AI, ML or DL techniques for improving your
IIoT implementations in your respective industries.
So, this is just the history.
AI has been popular since 1950’s, it is
still popular.
ML has been popular since the 1980’s, it
continues to be and deep learning, since last
few years may be 2006, 2007 onwards and so
on.
But ML, you know so all these things have
been there.
You know, AI has been there.
ML has been there, but in the only in the
recent times these have become more and more
popular due to a variety of reasons.
Due to the advent, the popularity of IoT for
instance, due to the popularity of autonomous
systems for example, you know autonomous cars,
self-driving cars which basically use a combination
of all of these technologies AI, ML, DL plus
different different IIoT technologies are
used.
And that is where this AI, ML, or DL are becoming
even more popular
So, these are 2 examples that I told you.
You know there are many more examples in the
current day world, where you know you have
to fall back on your previous you know previously
known technologies such as AI, ML and so on.
So, you know these are not new.
ML has been there, AI has been there for even
more for a longer time.
But only in the recent times, because of the
newer applications that are coming up, these
technologies have got renewed attractiveness
and are being used popularly in the industries.
So, we have understood the benefits of machine
learning.
But machine learning has its own limitations.
Machine learning algorithms are not useful
for high dimensional data.
So, clustering I have shown you x and y that
is fine, but if you want to increase the number
of dimensions then machine learning will gradually
become less useful.
Features will have to be explicitly mentioned
in machine learning, a type of machine learning.
But you know if you are talking about deep
learning, this is a newer technique where
you do not have to do these two.
This, this is basically where this is a new
learning technique, the deep learning technique
which is again based on machine learning,
but it tries to overcome some of the some
of the drawbacks of the, limitations of machine
learning.
Particularly you know DL is able to deal with
high dimensional data and that is where also
you know DL becomes much more the use utility
of DL becomes much more eminent.
And also you know feature extraction, manual
and explicit you know provisioning of these
features and so on those do not have to be
done in the case of DL.
So, so this is basically how you know these
ML and DL; they compared with one another
with respect to the volume of data.
So, you know if you see that you know ML and
DL you know with the increase in the number
of amount of data, volume of data, and the
dimensionality of the data rather you know.
DL gradually becomes more and more popular
and useful the performance improves whereas,
you know ML the performance of ML will gradually
come to a stagnation.
So, also the other limitation was that feature
identification and extraction is required
in ML whereas, it is not you know required
explicitly in DL.
So, deep learning, it is a subfield of machine
learning which is capable of learning the
right features on its own know.
On its own, it is able to learn the features.
Basically, it mimics the working function
of billions of neurons in our brain deep.
So, if you look at the neuron, neural structure
of the brain you know it is a very complicated
neural structure.
So, inspired from that neural structure, the
deep neural structure that is underneath different
different layers etc. it has been you know
deep learning has been inspired by that and
it builds upon that; it is little bittle little
different you know understanding brain is
not very easy.
So, you know it is inspired, but not exactly
you know brain-based neural structure that
is followed.
It is inspired, but is it its bit different,
but what DL does?
It learns the features on its own.
And this DL, gives improved performance with
respect to accuracy when the data volume increases
and the dimensionality of the data also increases.
So, one of the very popular techniques in
deep learning is to use deep neural network.
You know, so it deep in your network is again
based on neural network ANN’s, but its again
with deep deep.
I will show you how a deep neural network
looks like schematically, shortly.
But before that, so in a deep neural network,
the signals basically travel between different
neurons and layers of neurons in artificial
neural network.
In neural network, each neuron is assigned
with some weighted value, a weighted, a high
weighted neuron exerts more effect on the
next layer than the others and the final layer
combines all the weight inputs to emerge with
a result.
This is how it works.
So, diagrammatically I show you over here
that this DNN which is the deep neural network.
As you can see here, you have an input layer
and you have an output layer.
So, input layer basically takes the inputs,
the output through this you know this optimization
process is basically passed from the output
layer.
You can get the output from the output layer.
Now, in between are all these hidden layers,
these are the hidden layers where lot of you
know integrate relationships are there which
does these computations.
And you know you can increase the number of
these hidden layers and the more and more
you increase, the more and more computations
will be there.
The, but it is going to give you more accuracy
in general, but not in all cases, but in most
cases it is going to give you more and more
accuracy.
So, deep basically refers to the number of
hidden layers.
So, the more number of layers you have, the
deeper structure you are going to have of
the neural neurons.
So, it is said that the deep neural network
can have up to 150 hidden layers.
So, here we show only 2 layers, hidden layers,
but you know in a DNN up to 150 hidden layers
can be implemented.
So, let us take an analogy from real life
about how deep learning works.
Let us say that, we have to recognize whether
this is an apple or not right, so whether
this is an apple.
So, first you check the shape.
If you know, if you see that the shape is
what is desirable, if yes, if you after checking
that the shape is, then you check the color,
if the color is ok, then you check its taste,
you know you bite you have a bite on the apple
and then you check how it tastes.
If you see that the taste is ok, then you
confirm that it is an apple.
Right?
So, you make this confirmation or recognition
of an apple based on its shape, color, and
the taste.
And that is basically the nested hierarchy
concept.
This is the nested hierarchy concept, nested
hierarchy concept.
So, deep learning also follows the concept
of nested hierarchy.
And it makes the complex tasks into simpler
tasks.
Right?
So, this is just an analogy to you know make
you understand deep learning better.
So, difference between machine learning and
deep learning.
Deep learning is an end-to-end learning which
extracts features on its own.
This is the key thing, extraction of features
on its own.
On the other hand, in machine learning features
are to be explicitly manually mentioned.
In deep learning, the performance level often
improves as the size of the data increases.
Whereas, in machine learning, the shallow
learning basically converges.
So, we have 2 things.
We have IIoT, we have IIoT and we have deep
learning.
Both are very powerful technologies.
IIoT is helpful for improving the speed; whereas,
deep learning is useful for improving the
accuracy.
Now, if you put them together, what you get
is a multiplicative effect.
So, this multiplicative effect becomes a very
powerful thing which can help these manufacturing
industries in the factories to optimize their
product lines.
It can help in optimizing the energy consumption,
and to improve the transportation operations.
It can also help the system in for systems
shut down in the case of any kind of emergency
or eventualities.
So, together IIoT, deep learning together
makes things multiplicative in terms of the
benefits that can be obtained and together
you get a very powerful technology.
So, the reason for the usefulness of deep
learning in IIoT is like this.
So, the most important reasons that have made
deep learning so useful in the recent times
is, that only in the recent times, only in
the recent times.
The amount of labeled data that is required
has increased manifolds, has increased manifolds.
So, it has increased and is also available.
So, it is also available and at the same time,
in the recent times, the high end computational
power have also become quite high and at the
same time cheap.
So, high end computational power cheaper,
but available at low cost and coupled with
that the amount of large amounts of data have
the availability of that data has also increased
and the requirements for both of these has
also increased.
And consequently what we have is that together
people have realized nowadays that deep learning
has lot of benefits because of these necessities.
So, the critical requirements of deep learning
in IIoT are that nowadays we are talking about
solving critical issues such as, dealing with
large quantity of data and also dealing with
higher accuracy requirements higher quality
and so on.
So, deep learning basically provides the values
in terms of enabling the customers for identification
or recognition using technologies such as
cameras, sensors, etc., prediction or inference
of human behavior and autonomous decision
control.
So, these are the different benefits that
deep learning gives to the customers in different
business segments.
So, deep learning, there are uses of deep
learning a lot in the industries.
The company Toshiba of which we are very much
familiar, Toshiba uses something known as
the collaborative distributed deep learning
technology.
So, that is a technology that is used between
the edge and the cloud.
Edge means, some gateway device which can
do some per you know which can perform certain
processing.
So, between the edge and the cloud, this Toshiba
came up with a collaborative distributed deep
learning framework.
So, we are using this framework.
The learning process is performed in the cloud
for high end processing whereas; the inferencing
process is conducted in the edge for real
time processing.
Basic analytics will be done at the edge whereas
the deeper ones which require a lot of computation
and so on.
The higher in processing, higher in computation
will be done will be done at the cloud.
So, the Toshiba technology leads to improved
yield and productivity in the semiconductor
factory.
They adopted the drone navigation control
system to find damage in power transmission
lines, predicting behaviors of workers in
the warehouses through different wearable
devices, and forecasting power generation
in a solar power system.
So, another example is H2O platform that also
uses the deep learning framework.
Intel’s Nervana is a deep learning processor.
Zebra medical vision system, they are using
deep learning techniques for de different
problems in the medical domain.
So, these are some of these examples of deep
learning and where it is finding popularity.
Deep learning has become very popular in autonomous
driving vehicles.
Nowadays, deep learning along with IoT has
become very popular together.
They make these the self-driving cars a reality.
So, like this, there are many many different
utility of machine learning, deep learning,
and artificial intelligence and they are handshaking
with IoT and IIoT, which make them powerful
together in addition to having their individual
strengths.
So, with this, we come to an end.
Here are these different references like before
you are encouraged to go through these different
references and with this we come to an end
of the getting an overview of machine learning
and data science for IIoT.
Thank you.
