Below we have discussed the individual sale.
Now we are going to start these says to create network offsets just to avoid confusion with biological
neuron.
I'll be calling a neuron as perception runs only through perception.
From now on means any artificial neuron.
Now there are two ways we can stack cells violently or sequence in.
Let's see what happens when this tax is badly hit as a single positron with three inputs and one output.
Now we had a deposit brought bad luck to this cell also gets the same three inputs but it has a different
output why do we can keep on adding more cells better to these ones.
Maybe a third or fourth or even more than that we 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 phase equal to for these two variables will
become vital NYT although image definition needs a much more complex network.
By giving this example I wanted 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 parallel stacking of the artificial
neurons.
Now let's see going chilled stacking in the image above.
We have five inputs which we input to three parallel separate now the output from this set of perceptions
is taken and Fred as input to another set of valid perceptions.
Here I am inputting the output of these three to these four plus approx again I take the four outputs
of these perceptions and input these into this single perceptual.
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 parallel least tagged neurons is sequentially
given as input to the next set of parallels to act neurons.
Let's first understand the benefit of doing this.
That is why did we not just input all the five inputs into a single cell and use this output to predict
variable like how is stacking these additional sets of neuron helpful to we have this type of beta.
There are these two input variables maybe height and weight basis which we are trying to classify.
If the anyone in the room is a cow or a dog so cows generally like it they have more weight and more
heat than a dog.
And dogs generally like it that is they are represented by the red dot.
Now when we're classifying this sort 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 call and anything on the left side will be predicted
as a dog.
This is the capability of a single perceptual single perception can find out the best straight line
to classify the given data so if we have this problem using a single person wrong would suffice.
But the situation is more complex in fact in real life situations we'd never use neural networks when
we 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 line is Class 8 or is it a dot and anything in between these two lines is Class B or if a green
dot this type of classification situation cannot be handled by a single person on a netbook such as
the one shown on the right can easily handle it.
For example this bus neuron will fire.
That is give output as one ready point lays to the left of lane one and the second neuron will give
output as one ready point lays to divide off line to and this final neuron gives output as one when
any one of the two inputs is one you can polish the video.
Think about it for a couple of minutes and see how this small network is handling this special classification.
This is the power of a neural network in the network.
We created each neuron can focus on a particular feature of the object and not on the final output.
The final output will be predicted based is the desert of these features in this way.
Neural networks can do really sophisticated decision making with basic machine learning techniques such
as linear regression cannot do with good accuracy
before we move on.
Let's take a minute to discuss this.
Networks nominated.
This is a neural network now.
Each set of parallel neurons are called Live that first is the input layer the last is the output left
and these in-between decoding live this network had five inputs three cells inherently and one for inherently
two and one in the output layer so for brevity.
This network can also be called as a 5 3 4 1.
Network
ultimate is that the process information in this network is flowing in only the forward detection which
is why the network is also called a feed forward network in comparison.
If the output of one of these cells of that layer goes back as input to end of the scale of that literally
then it is called a cyclic network but it could have neural networks also known as audit.
In the example of cyclic network auditing are used in natural language processing and language modeling.
For now let's come back to standard feed for word network.
Now you can notice here that output from this said is going to force it.
These are not for different outputs.
It is only one output the same output is going as input in all these pulses also note that every neuron
in each layer is connected to every other neuron in the Edison forward layer 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 LICHTER I would like to tell you that within this short span of time in which we
covered this LICHTER 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 layers we have in the system the more deep our system is more that is capable of establishing
a 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 weight and biases to get good accuracy of prediction unanimously.