In this video, we will be introduced to the next section of this course, the main topic of this section
is convolutional neural networks.
We also call these CNN.
So basically a rough outline of this section is just two steps.
Number one, explain what a CNN is and how it works.
And number to show you how to use CNN to forecast and classify time series using various data sets as
per usual.
One interesting thing to look out for is whether or not CNN will be more powerful than what we learned
about in the previous sections.
OK, so as mentioned, the first part of this section will focus on what CNN is now, this may be surprising,
but the name actually tells you quite a bit.
A convolutional neural network is really just a neural network with convolutions.
Thus, a major component of this section will be to discuss what is convolution.
You already know the neural network part.
Therefore, all that remains is the convolution part.
And of course, how to put them together.
In my opinion, the most intuitive way to understand convolution is by understanding how they can be
used for images.
That is, pictures, pictures like Time series or objects that we can see on our computer screens.
So it is obvious when we do something to an image what kind of effect it has simply because we can see
it.
Now, this is the case for Time series, too, but Times series are a bit more abstract.
Because of this, we will first study convolution in the context of images after doing this and gaining
some intuition on what convolution actually does.
We will learn how they are applied to Time series.
Mathematically, convolution for a Time series is simpler than for images, but intuitively images are
simpler than Time series.
So there's a bit of a tradeoff there, depending on how you see things.
When you look at the math time series are simpler, but when you look at the intuition, images are
simpler.
Do note, however, that time series can be converted into images and then regular image convolutions
can be used.
Now, I want to provide a bit of warning to those beginner students who might be intimidated by math
convolution is a mathematical operation, however intenser flow, it's just one line of code.
So if you are intimidated by math, keep in mind that you can potentially just skip the explanation
for what convolution is and simply treat the code as if you are using APIs, which is in fact what is
being done.
If you decide to go this route, then you can skip to the lecture called CNN Code Preparation.
But if you do want to understand convolution and why they are useful in deep learning, then you should
watch all the lectures.