So in this first section, I'm going to spend
a few minutes discussing the difference
between artificial intelligence, neural
networks and machine learning. And the reason
we need to go into this is because we're
gonna be covering all of these topics
throughout this course. So it's vital that
you guys understand what these actually mean.
And you can kind of differentiate between
them. So that's what we're gonna focus on.
Now. Now, quick disclaimer here, just so
everyone's aware, I'm using something called
Windows Inc, this just default comes with
Windows, I have a drawing tablet down here.
And this is what I'm going to be using for
some of the explanatory parts. But there's no
real coding, just to kind of illustrate some
concepts and topics to you. Now I have very
horrible handwriting, I'm not artistic,
whatsoever, programming is more definitely
more of my thing than, you know, drawing and
doing diagrams and stuff. But I'm going to
try my best. And this is just a way that I
find I can convey information the best to you
guys. So anyways, let's get started and
discuss the first topic here, which is
artificial intelligence. Now, artificial
intelligence is a huge hype nowadays. And
it's funny because a lot of people actually
don't know what this means. Or they tried to
tell people that what they've created is not
artificial intelligence, when in reality, it
actually is. Now the kind of formal
definition of AI and I'm just gonna read it
off of my slide here to make sure that I'm
not messing this up, is the effort to
automate intellectual tasks normally
performed by humans? Now, that's a fairly big
definition, right? What is considered an
intellectual task? And, you know, really,
that doesn't help us too much. So what I'm
going to do is bring us back to when AI was
first created to kind of explain to you how
AI has evolved and what it really started out
being. So back in 1950, there was kind of a
question being asked by scientists and
researchers, can computers think, can we get
them to figure things out? Can we get away
from hard coding? And you know, having like,
Can we get a computer to think and it do its
own thing? So that was kind of the question
that was asked. And that's when the term
artificial intelligence was kind of coined
and created. Now back then AI was simply a
predefined set of rules. So if you're
thinking about an AI for maybe like tic tac
toe, or an AI for chess, all they would have
had back then, is predefined rules that
humans had come up with and typed into the
computer in code. And the computer would
simply execute those set of rules and follow
those instructions. So there was no deep
learning machine learning crazy algorithms
happening, it was simply if you wanted the
computer to do something, you would have to
tell it beforehand, say you're in this
position. And this happens, do this. And
that's what AI was. And very good AI was
simply just a very good set of rules where a
ton of different rules that humans had
implemented into some program, you could have
AI programs that are stretching, you know,
half a million lines of code, just with tons
and tons and tons of different rules that
have been created for that AI. So just be
aware that AI does not necessarily mean
anything crazy, complex, or super
complicated. But essentially, if you're
trying to simulate some intellectual task,
like playing a game that a human would do
with a computer, that is considered AI, so
even a very basic artificial intelligence for
Tic Tac Toe game where it plays against you,
that is still considered AI. And if we think
of something like Pac Man, right, where we
have, you know, our little ghosts, and this
will be my rough sketch of a ghost, we have
our Pac Man guy who will just be this, would
we consider this ghost AI, what it does is an
attempt to find and kind of simulate how we
get to Pac Man, right. And the way this works
is just using a very basic pathfinding
algorithm. This has nothing to do with deep
learning, or machine learning or anything
crazy. But this is still considered
artificial intelligence, the computers
figuring out how it can kind of play and do
something by following an algorithm. So we
don't necessarily need to have anything
Crazy, Stupid complex to be considered AI, it
simply needs to just be simulating some
intellectual human behavior. That's kind of
the definition of artificial intelligence.
Now, obviously, today, AI has evolved into a
much more complex field where we now have
machine learning and deep learning and all
these other techniques, which is what we're
going to talk about now. So what I want to
start by doing is just drawing a circle here.
And I want to label this circle and say, A, I
like that.
So this is
going to define AI, because everything I'm
about to put inside of here is considered
artificial intelligence. So now, let's get
into machine learning. So what I'm going to
do is draw another circle inside of here. And
we're going to label this circle, ml for
machine learning. Now notice I put this
inside of the artificial intelligence circle.
This is because machine learning is a part of
artificial intelligence. Now, what is machine
learning? Well, what we talked about
previously, was kind of the idea that AI used
to just be a predefined set of rules, right?
Where what would happen is, we would feed
some data, we would go through the rules by
and then analyze the data with the rules and
then we'd spit out some output which would be
you know what we're going to do. So in the
classic example of chess, say we're in check,
what we pass that board information to the
computer, it looks at it sets of rules, it
determines we're in check and then it moves
us somewhere else. Now what is machine
learning in contrast to that Machine learning
is kind of the first field that actually
figuring out the rules for us. So rather than
us hard coding the rules into the computer,
what machine learning attempts to do is take
the data and take what the output should be,
and figure out the rules for us. So you'll
often hear that, you know, machine learning
requires a lot of data. And you need a ton of
examples and, you know, input data to really
train a good model. Well, the reason for that
is because the way that machine learning
works is it generates the rules for us. We
give it some input data, we give it what the
output data should be. And then it looks at
that information and figures out what rules
can regenerate, so that when we look at new
data, we can have the best possible output
for that. Now, that's also why a lot of the
times, machine learning models do not have
100% accuracy, which means that they may not
necessarily get the correct answer every
single time. And our goal when we create
machine learning models is to raise our
accuracy as high as possible, which means
it's going to make the fewest mistakes
possible. Because just like a human, you
know, our machine learning models, which are
trying to simulate, you know, human behavior
can make mistakes. But to summarize that,
essentially machine learning the difference
between that and kind of, you know,
algorithms and basic artificial intelligence
is the fact that rather get that rather than
us the programmer giving it the rules, it
figures out the rules for us. And we might
not necessarily know explicitly what the
rules are when we look at machine learning
and create machine learning models. But we
know that we're giving some input data, we're
giving the expected output data, and then it
looks at all of that information does some
algorithms, which we'll talk about later on
that and figures out the rules for us. So
that later when we give it some input data,
and we don't know the output data, it can use
those rules that it's figured out from our
examples and all that training data that we
gave it to generate some output. Okay, so
that's machine learning. Now we've covered AI
and machine learning. And now it's time to
cover neural networks or deep learning. Now,
this circle gets to go right inside of the
machine learning right here, I'm just going
to label this one n n, which stands for
neural networks, neural networks get a big
hype, they're usually what the first, you
know, when you get into machine learning, you
want to learn neural networks are kind of
like neural networks are cool, they're
capable of a lot. But let's discuss what
these really are. So the easiest way to
define a neural network is it is a form of
machine learning that uses a layered
representation of data. Now, we're not going
to really understand this completely right
now. But as we get further in, that should
start to make more sense as a definition. But
what I need to kind of illustrate to you is
that in the previous example, where we just
talked about machine learning, essentially
what we had is we had some input bubbles,
which I'm going to define as these, we had
some set of rules that is going to be in
between here, and then we have some output.
And what would happen is we feed this input
to this set of rules, something happens in
here, and then we get some output. And then
that is what you know, our program does,
that's what we get from the model, we pretty
much just have two layers, we have kind of
the input layer, the output layer, and the
rules are kind of just what connects those
two layers together. Now in neural networks,
and what we call deep learning, we have more
than two layers. Now I'm just trying to erase
all this quickly. So I can show you that. So
let's say and I'll draw this one another
color, because why not? If we're talking
about neural networks, what we might have,
and this will vary, and I'll talk about this
in a second is the fact that we have an input
layer, which will be our first layer of data,
we could have some layers in between this
layer, that are all connected together. And
then we could have some output layer. So
essentially, what happens is, our data is
going to be transformed through different
layers, and different things are going to
happen, there's gonna be different
connections between these layers. And then
eventually, we will reach an output. Now it's
very difficult to explain neural networks
without going completely in depth. So we'll
cover a few more notes that I have here.
Essentially, in neural networks, we just have
multiple layers. That's kind of the way to
think of them. And as we see machine
learning, you guys should start to understand
this more. But just understand
that we're dealing with multiple layers. And
a lot of people actually call this a multi
stage information extraction process. Now, I
did not come up with that term and guts from
a book or something. But essentially, what
ends up happening is we have our data at this
first layer, which is that input information,
which we're going to be passing to the model
that we're going to do something with, It
then goes to another layer where it will be
transformed, it will change into something
else using a predefined kind of set of rules
and weights that we'll talk about later. Then
it will pass through all of these different
layers were different kind of features of the
data, which again, we'll discuss in a second
will be extracted will be figured out will be
found until eventually we reach an output
layer where we can kind of combine everything
we've discovered about the data into some
kind of output that's meaningful to our
program. So that's kind of the best that I
can do to explain neural networks without
going on to a deeper level. I understand that
a lot of you probably don't understand what
they are right now and that's totally fine.
But just know that there are layered
representatives. data, we have multiple
layers of information. Whereas in standard
machine learning, we only have, you know, one
or two layers. And then artificial
intelligence in general, we don't necessarily
have to have like a predefined set of layers.
Okay, so that is pretty much it for neural
networks, there's one last thing I will say
about them is that they're actually not
modeled after the brain. So a lot of people
seem to think that neural networks are
modeled after the brain and the fact that you
have neurons firing in your brain. And that
can relate to neural networks. Now, there is
a biological inspiration for the name neural
networks in the way that they work from, you
know, human biology, but is not necessarily
modeled about the way that our brain works.
And in fact, we actually don't really know
how a lot of the things in our brain operate
and work. So it would be impossible for us to
say that neural networks are modeled after
the brain, because we actually don't know how
information is kind of happens and occurs and
transfers through our brain. Or at least we
don't know enough to be able to say, this is
exactly what it is a neural network. So
anyways, that was kind of the last point
there. Okay, so now we need to talk about
data. Now data is the most important part of
machine learning and artificial intelligence,
neural networks as well. And it's very
important that we understand how important
data is and what the different kind of parts
of it are, because they're going to be
referenced a lot in any of the resources that
we're using. Now what I want to do is just
create an example here, I'm gonna make a data
set that is about students final grades in
like a school system. So essentially, we're
gonna make this a very easy example, we're
all we're going to have for this data set is
we're going to have information about
students, so we're gonna have their midterm
one grade, their midterm to grade, and then
we're gonna have their final grade. So I'm
just gonna say mid term, one. And again,
excuse my handwriting here, it's not the
easiest thing to write with this drawing
tablet. And then I'll just do final. So this
is going to be our data set. And we'll
actually see some similar data sets to this
as we go through and do some examples later
on. So for student one, which we'll just put
some students here, we're going to have their
midterm one grade, maybe that's a 70, their
midterm to grid, maybe that was an 80. And
then let's say their final was like their
final term grade, not just the mark on the
final exam, let's give them a 77. Now for
midterm one can give someone a 60, maybe we
give them a 90, and then we determine that
the final grade on their exam was, let's say,
an 84. And then we could do something with
maybe a lower grade here, so 4050, and then
maybe they got a 38, or something in the
final grade. Now, obviously, we could have
some other information here that we're
admitting like maybe there was some exams,
some assignments, whatever some other things
they did that contributed to their grade. But
the problem that I want to consider here is
the fact that given our midterm, one grade
and our midterm to grid and our final grade,
how can I use this information to predict any
one of these three columns. So if I were
given a student's midterm, one grade, and I
were given a student's final grade, how could
I predict their midterm to grade. So this is
where we're going to talk about features and
labels. Now, whatever information we have
that is the input information, which is the
information we will always have that we need
to give to the model to get some output is
what we call our features. So in the example
where we're trying to predict midterm two,
and let's just do this and highlight this in
red, so we understand what we would have as
our features, our input information are going
to be midterm one and final. Because this is
the information we're going to use to predict
something it is the input, it is what we need
to give the model. And if we're training a
model to look at midterm one and final grade,
whenever we want to make a new prediction, we
need to have that information to do so. Now
what's highlighted in red, so this midterm
two here is what we would call the label or
the output. Now, the label is simply what we
are trying to look for or predict. So when
we talk about features versus labels,
features is our input information, the
information that we have that we need to use
to make a prediction. And our label is that
output information that is just representing
you know what we're looking for. So when we
feed our features to a model, you will give
to us a label. And that is kind of the point
that we need to understand. So that was the
basic here. And now I'm just going to talk a
little bit more about data because we will
get into this more as we continue going, and
about the importance of it. So the reason why
data is so important is this is kind of the
key thing that we use to create models. So
whenever we're doing AI and machine learning,
we need data pretty much unless you're doing
a very specific type of machine learning and
artificial intelligence, which we'll talk
about later. Now for most of these models, we
need tons of different data, we need tons of
different examples. And that's because we
know how machine learning works now, which is
essentially, we're trying to come up with
rules for a data set. We have some input
information, we have some output information
or some features and some labels, we can give
that to a model and tell it to start training
and what it will do is come up with rules
such that we can just give some features to
the model in the future and then it should be
able To give us a pretty good estimate of
what the output should be. So when we're
training, we have a set of training data. And
that is data where we have all of the
features and all of the labels. So we have
all of this information, then when we're
going to test the model or use the model
later on, we would not have this midterm two
information, we wouldn't pass this in the
model, we would just pass our features, which
is midterm one and final and then we would
get the output of midterm two. So I hope that
makes sense. That just means data is
extremely important. If we're feeding
incorrect data or data that we shouldn't be
using to the model that could definitely
result in a lot of mistakes. And if we have
incorrect output information or incorrect
input information that is going to cause a
lot of mistakes as well, because that is
essentially what the model is using to learn
and to kind of develop and figure out what
it's going to do with new input information.
So anyways, that is enough of data. Now let's
talk about the different types of machine
learning.