>> You're not going to want to miss this episode of the AI Show
coming to you because it's special from Microsoft Ignite.
Francesca Lazzeri tells us
all about the difference between machine learning,
deep learning, and what it means for AI. We'll see you then.
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>> Hello and welcome to this special edition of
the AI show where we're live.
Well, we're not live, because you're watching a recording,
but we're live right now at Ignite.
I have my esteemed dear colleague, Francesca Lazzeri.
We actually work together,
but she's much classier than I am.
How are you doing, Francesca?
>> I'm good, I'm great,
and thank you so much for having me on the AI show.
>> Fantastic. Well, look, one
thing we're going to talk about because we like to
keep these very crisp is machine learning,
deep learning, what's the difference,
and we're hoping you're going to help us out.
>> Yeah. So this is a great question because
actually all customers I have been
working with ask me the same question.
We want to do something with our data,
we want to use our data to answer our business question,
but how can we use machine learning,
and what's the difference between the machine learning,
deep learning, what is AI?
So I wrote an article about these so I
can give them an answer every time they ask me this question.
This article really compare the three different area.
So let's start with the central one,
with the core one, which is machine learning.
So I think about the machine learning as
a portfolio of algorithms.
You have to think about algorithms as a statistical functions,
a mathematical functions that use your data
to learn something and give you a result.
Now, what is AI?
AI is about consuming the machine learning.
So you will build
the application on top of the machine learning model,
and you consume these machine learning models,
these AI application over time.
So it's a really intelligent way of consuming machine learning,
that's the tarot reading your data.
Then deep learning, deep learning is just a type machine learning.
It's a very interesting type of machine learning
because it's based on what we call the neural networks.
So there are many different networks
and since we have different networks,
different layers also as well,
they are called the deep learning.
Thereby, it is just a type.
It's just a topology of the machine learning.
So you don't have to think about
machine learning and deep learning
as something that they are completely different.
They are in the same area and AI,
of course, is using both of them.
>> I see. So machine learning then is a discipline whereby
we extract information or algorithm from data,
I think it's what you said.
Deep learning is a subset of that,
that has a special kinds of algorithm.
>> Yes.
>> So at what point can I be like I am using deep learning?
How do you know when you're there?
>> So you are there when, of course,
it depends a lot on the type of the data that you have,
the type the questions that you want to answer.
If you have, for example,
a huge amounts of data sourced from a hardware perspective,
you need, for example,
to use GPUs instead of CPUs.
I would say, these are all indicators that tells you
that you are entering the deep learning area.
>> That's fantastic.
>> Of course, in terms of also different APIs
that you can use in order to facilitate your deep learning work.
There are so many difference and you can use all of them
on Azure, like TensorFlow, Keras.
So it's really up to you,
and on the type of data,
and the type of question that you're using.
Another interesting part is
that there are multiple types of neural networks.
So this means that many different
print type of deep learning algorithms,
there are convolutional neural networks.
>> Which I like. I'm a vision guy so I like those.
>> Recurrent neural network.
>> Which I'm trying to understand,
so you're going to help us out maybe at one point.
>> Of course.
>> Yeah.
>> Then under recurrent neural networks, for example,
we have the LSTM and GRU.
>> GRUs.
>> Exactly. They are very good with the sequential type of data.
So for time series scenarios wherein you want
to build the end-to-end time series forecasting solution,
LSTM can be very, very hard work.
But again, it depends a lot on the problem that we want to solve,
on the size of your data,
on the type of data that you want, and then,
of course, how you're going to
build these end-to-end architecture.
>> Awesome. So it turns out that all of
these stuff, she broke down.
She didn't write it down and just in a day,
by screw it down in our docs.
So why won't you tell us about the doc
that you own and how people can go and read it.
>> Yes. So this is a very nice doc because you can consume it in
a very nice and easy way in the sense
that we didn't put too much information about it.
We just selected the most important information.
So the doc starts with
the mean difference between artificial intelligence,
machine learning, and deep learning.
It explain everything here.
Then if you keep on,
we created this very nice stable that's compares
machine learning versus deep learning
in terms of number of data points,
this is exactly the question that you were asking,
hardware dependencies, the featurization process.
Because, for example, with machine learning,
you need to do what we call the feature engineering.
You need to get your dataset,
bigger, richer by doing the feature engineering.
With deep learning, you don't need to.
So there are some differences also for these: learning approach,
execution time, and then of course, the output.
So this is a nice simple table that compares the two.
Then, if you keep scrolling,
you can see that there are
different type of explanation for deep learning.
What you can do with deep learning, for example,
there is the named-entity recognition use case,
object detection use case, image caption generation,
machine translation, text analytics,
and then there is a short explanation
of artificial neural networks,
and we already mentioned some of them.
Feedforward neural network, recurrent neural network,
and a convolutional neural network.
>> That's awesome.
>> So again, you can see that using
a very nice article that people can enjoy reading.
Then at the end, you have
also lot of different resources on how you can.
>> How you can do it.
>> Exactly, how you can do deep learning on Azure.
>> Awesome. So what we'll do is,
we'll make sure that this particular article is in
the show notes so you can actually
go and read it and get a sense for it.
I actually love it.
I'm computer vision person,
you obviously know all things: time
series, forecasting, and sequence.
So I'd love to learn more about that.
Thank you so much for spending some time with us.
Thank you so much for watching
and learning all about machine learning,
and deep learning, and AI,
what it means, and a special doc with the link below.
Again, thanks for watching this special episode of
the AI Show coming to from Ignite. We'll see you next time.
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