Now, we have discussed the individual sale.
Now we are going to say that this is to create network offsets.
Just to avoid confusion with biological neutron.
I'll be calling a neutron as Perceptron only.
So Perceptron.
From now on means any artificial neutron.
Now, there are two ways we can stack sits badly or sequentially.
Let's see what happens when these taxes, Badali.
Head is a single Perceptron, the three inputs and one output.
No, we had an added Perceptron that luck do it.
This cell also gets the same three input, but it has a different output.
Why do.
We can keep on adding motives to these words, maybe a third or fourth or even more than that, we'll
just start getting new output.
Or in other words, we can predict for multiple output using the same input features.
For example, when we are doing image recognition and we are trying to find out a face of a person,
he may also want to find the X and Y coordinate of that face to.
For these two variables will become vital NYT.
Although it was the commission needs a much more complex network by giving this example.
I want to make the point that neural networks are not bound to only one output.
With the same input, you can get multiple output because we can do battler's stacking of the artificial
neurons.
Now, let's see.
He's going to stacking.
In the image above, we have five inputs which we input to three badland percipient.
Now, the output from this set of Perceptron is taken and Frager's input to another set of Belet Perceptron
hit.
I'm inputting the output of these three to these four separate.
Again, I take thee forward our ports of these Perceptron and put these into this single Perceptron.
Lastly, the singleton is giving out one single output, which is the variable which we want to predict.
So this is sequential stacking in which the output of one set of Ballylee stack neurons is sequentially
given as input to the next set of badly stacked neurons.
Let's first understand the benefit of doing this.
That is why did we not just input all the five input into a single cell and use this output to predict
the variables like.
How is stacking these additional sets of Meuron helpful?
Later, we have this tape of Peter.
There are these two input variables.
Maybe height and weight.
This is what we are trying to classify.
If the anyone in the room is a cow or a dog.
So calls generally like it.
They have more weight and more height than a dog.
And dogs generally like it.
That is the other represented by the red dot.
Now, when we're classifying this set of data, we can have a linear separator that is a straight line
to separate these two classes.
Anything on the right side will be predicted as a go.
And anything on the left, fate will be predicted as a dumb.
This is the capability of a single Perceptron.
A single Perceptron can find out the best straight line to classify given data.
So if we have this problem.
Using a single Perceptron would suffice.
But Bortolotti situation is both complex.
In fact, in VLF situations, we'd never use neural networks when we'd need to classify a photo situation.
As simple as this.
The real life situations for neural networks is often more complex.
Let me complicate the example a little bit.
What if we wanted to classify objects which have this distribution?
So anything to the left of the first name and anything to the date of the second lane is Class eight
or is it a dark?
And anything in between these two lines is Glasby or is a green dot.
This type of classification situation can not be handled by a single Perceptron.
A netbook such as the one shown on the right can easily handle it.
For example.
This first neuron will fire, that is give output as one rendi point lays to the left of lane one.
And the second neuron will give output as one windy point lays to deviate off line to.
And just finally, Ron, give outwards one when any one of the two impulses, one.
You can post the video here.
Think about it for a couple of minutes and see how the small network is handling this special classification.
This is the part of a neural network.
In the network we created, each neutron can focus on a particular feature of the object and not on
the final output.
The final output will be predicted.
Besides the desert of these features.
In this way, neural networks can do really sophisticated decision making with basic machine learning
techniques such as the integration did not do with good accuracy.
Before we move on, let's take a minute to discuss this network's nominated.
This is a neural network.
Now, each set of badland neurons are called lid's.
The first is the input layer.
The last is the output limit.
And these in-between these live.
This network had five inputs.
Three in Italy and one in Italy, two and one in the outwardly.
So for brevity, this network can also be caught as a five, three, four, what network?
Ultimate is that due process information in this network is flowing in only Deepwater Derickson.
Which is why so it didn't work.
It's also called Feed Forward.
Network.
In comparison, if the output of one of these cells of that list goes back as input to end of the sale
of that, literally, then it is called a cyclic network.
They couldn't mutilate books.
Also known as AUDIN in the example of Liquid Network.
Auditing are used in natural language processing and language modeling.
For now, let's come back to a standard feet forward.
Take a look.
Now, you can notice here that output from this in.
It's going to force its.
These are not four different outputs.
It is only one output.
The same output is going as input and all the forces.
Also note that every neuron in Easley, it is connected to every other neuron in the dissin forward
lere.
Therefore, this network is fully connected.
If somehow some links were missing, we call it possibly connected.
But for most practical purposes, we use a fully connected network.
Before I close this lectern, I would like to tell you that within this short span of time in which
we covered this lecture, we have entered devoid of deep learning.
Such artificial neural networks is what deep learning is made up of.
Basically, think of this like a system which loans the relationship between input and output.
The more lives we have in this system, the more deep our system is and more it is capable of establishing
complex relationship between input and output.
So I hope that you understand the basics of neural networks.
In the next lecture will go deeper and see how these networks process the output and find the optimum
values of VATE and biases to get good accuracy of prediction.
See you in the next picture.