In this video, we will be introduced to the next section of this course.
The main topic of this section is recurrent neural networks.
We also call these aren't ends.
So what makes Arnold special?
Well, consider the neural networks we have studied so far.
They all have this one peculiar property, which makes them unlike biological neural networks.
And this is that all the information goes in the same direction from left to right.
So here's a question to consider why not simply have neuronal connections which are less restrictive,
for example, ones that can go backwards when we create connections that go backwards in a specific
way with a time delay of one step?
We call these recurrent neural networks.
Now, please note that there are all kinds of ways we could make new connections, but only this specific
way is what we call a recurrent neural network.
OK, so this diagram is pretty messy, but what you should notice is that every hidden unit has an arrow
that goes back towards all other hidden units, including itself.
The most common diagram, which looks like this is a bit cleaner but actually does not reflect how the
connections are made in this case.
Notice how each heading unit loops back only to itself.
This is not exactly what we want.
So let's do a brief outline of this section as per usual.
This section will start with the theory and concepts behind Arnett's.
Well, first study the simple Arnon, also known as the eminent in this will form the basis for everything
else we do.
Once we understand the simple answer and we'll do a small code exercise which will reinforce your understanding.
As you recall, one of the major themes of this course is thinking about shapes.
This section will really test your ability to understand this.
Now, when it comes to art ends, the simple answer is rarely ever used any more.
For the time being, the LACMA is the most common type of art in unit, so we'll study the LACMA and
the closely related GIU.
You'll see why the LACMA and Giu are needed and what advantages they have over the simple art in the
next step will be to put everything into practice with various data sets as usual.
Now I want you to remember one thing, which is that all data is the same.
So although earnings can be applied to all of the data sets we've looked at so far, we of course do
not have time for the mall.
So if there's anything you want to try them on, please remember that no code has to change.
Simply switch one data set out for another.
And of course, there will be plenty of other configuration options to choose from as well.
So please feel free to fiddle around and test things out.
Now, I want to make a special note that like with convolution, you don't have to go through the theory
lectures if you find the math is intimidating for you.
Instead, if you prefer to treat the code simply like an API, you can do that as well.
So if that's the strategy you wish to take, please skip to the code preparation.
In my opinion, almonds contain a bit less math and science, so they might be easier to understand.
It's mostly about understanding the diagrams and the flow of the data.
However, understanding the equations you see will make it much simpler.
This actually goes back to our discussion on machine learning, where I said that one of the most important
expressions you will learn is W Transpose X plus B for Arnaz.
We are going to see this in multiple places at once.
Recall that I advised you not to think about this as math, but merely a pattern or an operation.
So once you see things from this perspective, it makes it easier since you can think at a higher level
about how these more fundamental operations can be connected together to make new things.
In fact, I encourage you to watch this section with the perspective that there actually is no math
at all, although you're going to see some equations that look like math.
It's actually very interesting to note that you don't have to actually do any math.
The section is more about understanding things at a higher level and how different building blocks fit
together to create bigger systems.
Of course, it's possible you might be so intimidated by the math that you'll see it in.
All, this advice will go out the window so you can make that determination for yourself.