Hello and welcome to this tutorial here we will learn the basic building block of artificial neural
network.
That is the new ROM.
So this is the actual biological structure of a neuron.
This is the main part of a neuron.
The body of neuron has branches and these branches are called Dad's then lives.
And it has a long day, which is called as Exon for a neuron.
The androids are the receivers of Signal and Axon is the transmitter.
No, no, Don.
An important point here to function.
A human brain.
A single neuron can do much to do that.
There are billions of neurons.
These all neurons work together, transmit and receive signals.
And as a result, human brain functions properly.
Now the deep learning and artificial intelligence scientists are inspired by the functioning of brain.
And they are trying to mimic the functioning of the brain and to mimic the functioning of a brain.
They are using neurons.
So this is a single neuron.
It is also called as node.
Now this neuron is receiving input signals like daybreaks to receive signals after processing the input
signal.
This neuron is transmitting the output that is output signal.
So here we are trying to mimic a biological neuron.
Now, this single neuron is receiving input signals from other neurons.
These other neurons are denoted as X1, x2 and up to X and.
And the neurons transmit signal to sign UPSs after processing the input signal from other neurons.
This neuron transmits output.
This output signal by this neuron is denoted as VI.
So the single neuron receives the input signal from other neurons.
Then it processes that input signal.
And at the end it transmits that signal as an output.
This is the working of a single neuron.
No distinguish neuron is receiving input signals from other neurons, right?
No, no, don't hear the input neurons that are transmitting signal to a single neuron are independent
variables.
These are the independent variables from a single observation.
You can think it as a role in an Excel sheet, like the information about an employee in a room.
Age, employee I.D., date of work and so on.
This is a single observation.
Remember this independent variable, one independent variable to add up to independent variable X and
after processing the input signals B how?
Output here.
That is why this output can be a continuous value like pride.
All this output can be a binary output.
Like s no.
And other than these two types, the output can be a categorical output.
So this is a signal of their vision that a neuron is processing here.
Throughout the process, we are processing a single observation here.
And at the end, we have output from that single observation.
As we had discussed earlier, neurons transmit and receive signals through design offices.
In deep learning did sign.
These are called as.
As you can see here.
Wait one wait to up to it.
And by adjusting the weights, a neural network, Lon's as of now, just remember that to the base,
a neural network alone.
Let us understand what is happening inside a mood on inside a neutron.
We did some of these all raids are calculated.
And after that activation function is applied here, the activation function calculates debated sum
of input and then it adds the bias.
And after that, it decides whether a neutron should be activated or not.
In simple words, we can say that the activation function decides whether a signal should be passed
further or not.
So this is how a signal is processed by a neuron.
It receives signal calculous, debated some outplays activation function.
And at the end it transmits the output.
So theoretically, this is all about the working of a single neuron.
As you can see here, this is a neural network and this neural network is made up of multiple neurons.
There is an input layer here, then layers.
And the output layer here.
Each neuron is receiving input signal processing that input signal with the help of activation function.
And at the end, transmitting the same.
This is how a neural network works.
So this introductory tutorial about the new long and neural network NCAR.
I will see you in the next one.
buildOn, happy learning.