AI Show – Deep Learning vs. Machine Learning

AI Show – Deep Learning vs. Machine Learning

November 26, 2019 1 By Stanley Isaacs


>>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. [MUSIC]>>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. [MUSIC]