Now, let's see which areas can we use artificial neural networks.
These are the techniques of neural networks.
You have classification neural networks and neural nets for can be trained to classify given pattern
or data set into predefined classes.
It uses feet forward for network for this purpose.
Another one that we have here is prediction.
Neural network and neural network can be trained to predict and outputs that are expected from giving
input.
We have several examples here, like a stock market prediction, weather prediction, traffic prediction
and more.
Next one to use clustering neural network and vector quantization clustering neural network is just
a network that can be used to identify a unique feature of the data and then classify them into different
categories without any prior knowledge of the data in this area.
We have different algorithm like competitive networks, adaptive reason and temporary networks covered
in self organizing maps and more.
But let me just give you a better example for clarifying their purposes.
Suppose you have ten samples of tweeze or geographical places or 10 different patients.
We have some information from them.
And these systems, these methods, these techniques are helping us to classify them with a number.
So classifying them into different sets and finally represent each feature of them with a number.
That's what we call here.
Victor Quantization, costarring neural network vector quantization and classifying neural networks.
They are pretty the same, which of course some differences, but we can use all of them for classifications.
Next one is feature extension.
Any major coordination.
Let's say you are driving in a highway and there are some cameras to detect the plate number of your
car.
This is an example of a plate number, for example, here we have a B and then seven to zero, Avago
is giving somehow consciousness to this camera to detect, to understand this plate number, to take
the last digit and decide to issue a penalty for this car or not based on these last number and decoding
data, if the last digit of the plate number is zero and one, then this car cannot drive on Monday.
The first thing for this camera is to detect is zero, because this is just an image for a camera.
A camera doesn't have any understanding of this image.
We can train the camera using different image recognition techniques to detect that this is a zero.
After that, the camera needs to decide that this car can go outside based on this plate number or not.
This is the feature extraction part.
So we are just extracting a feature from this model.
Next one that we have here is association.
Neural networks.
Neural network can be trained to remember the particular pattern so that when the noise pattern's presented
to the network, the network associates it with the closest one in the memory or disregarded Hockfield
networks which perform recognition, classification and clustering and more.
We can use these techniques for reading different handwritings, for example, and just writing LATAPY
like that.
There are several sets in these networks memory and the network is going to compare it with one of the
models on its memory.
But what if I won't let her be like that?
So again, this network is comparing this model with the closest one which it has in its memory.
So this is how the association neural network works.
There are several more techniques of neural nets for each of these are the most popular one.
And in this course, we are going to focus on classification, neural network technique.