OK, so we are now entering the next section of this course, so this section is where we begin our
study of deep learning for a time series analysis.
We'll start with learning about Anan's, which stands for artificial neural networks.
Now, technically, if all you wanted to do was use a simple and then you wouldn't really need the section,
you could just use the MLP.
And I learn.
But with deep learning, it's worth knowing about how it actually works so that you can go beyond simple
and ends up to more complex architectures such as CNN.
Zanón ends for this will be using Tenzer flow.
So essentially the deep learning sections boil down to two components.
No.
One, understanding how and why the model works.
And number two, how to implement it using tensor flow.
So the outline for this section is as follows.
We'll start with understanding the basic concept of the neuron.
Basically, this isn't anything new in terms of models, but it's a new perspective on how to look at
the linear models we've already used.
The next step is to combine neurons, to get neural networks.
Once we understand that, we'll learn about how to think of what animals do from a geometrical perspective.
That is why our neural networks, nonlinear, will then discuss different activation functions and how
neural networks can be used for multiclass problems.
The next step will be to use interns for multiple data sets.
We'll start with airline passengers, which has been our benchmark data set for this course.
We'll also look at stock predictions and we'll see that this doesn't require any new code.
The next step will be to look at a new data set called Human Activity Recognition for this data set.
We'll get to build a time series classifier, which can take as input a multivariate time series.
This is significant for two reasons.
Firstly, we haven't yet seen any classifiers that can handle multivariate time series in a non way.
Theoretically, you could just use the machine learning models we discussed previously, but that would
be naive since it treats every sample from every timestep and every channel the same way.
This leads us to our second point, which is that in order to solve this problem, you'll need to learn
about a new neural network architecture.
OK, so this is not your typical intro to Andsnes.
This will include a new way to build in ends that can handle multivariate time series, not naively.
As a final note for this lecture, I want to mention that my sense of the two course is what I call
a soft prerequisite for the deep learning sections.
It's not critical because we will go over all the necessary details.
However, if you want to have a better understanding of what we are doing, then you'll want to have
that experience.
Alternatively, my torch chorus or my in-depth deep learning series are also viable prerequisites.
And this is because if you've already learned PI talk, you should find tensor flow to quite easy.