This is a block diagram off of a multilayer Perceptron where we have a picture of X for our input,
we have here twin neurons and one into an entry, the output of this neuron is Y one, the output of
neuron two is Y two.
And similarly, we have Z for our output of these.
System.
Let me write down why one as the function of Ivanoff here we have w transpose w one transpose, we're
going to just label it with W one X plus V A bias of B for this now.
And this is for the first Nahrin and one to show the output of first Nahrin.
For the second number we have Y two as an output two and this one is W to transpose X.
Plus B, let's just label them B one and B to.
Now, for the Z function, we can also write Z is equal to F of three this time W three, transpose
X or let's say why it is better to write it with Y plus MI three and four F of three.
We can write W three one y one plus W three to buy one y two this time plus B three.
So we just expand this W three Y and now here we can continue and write as Z F of three.
If we just expanded here I can write it as W three one f one F funny's W one transpose X plus B one
and then it just replace it with its value plus W twe two, F of two and F two.
Here is W to transpose X plus B to just don't forget this Vetri plus B tree and just close to book.
So we see that multi-layer Perceptron is nothing but a function.
But why did we do that and why do we need to take a look at this one in this way?
If you're using a software, if we are using different techniques of software, we don't really need
to write down this function.
But it's a very good example to understand what is a simple Neren and what is this algorithm that later
we are going to work with how they work and what our day basson.
If you are really looking into finding a mathematical function, we have different tools as simple tools
like a curving fitting tools where you can just walk me to functions and right on separating two sets
of data.
I hope that you can understand now the fundamental of Narron and what is a neuron?
How can we represent a narrow made a mathematical function and hard working?
And why do we need to use multilayer Perceptron in the next session?
We're going to discuss about hardware implementation often never.