Now, in this lecture, we're going to focus on every note, which we call Nilgun.
So we've seen these notes in the previous lectures and this is specifically a note in the hidden layer.
And as you can see, there is three inputs connecting to it.
So if you remember in the previous example, we have three input layers, three input nodes, which
are connected to four of these hidden layer.
So now we are representing only one node of this hidden layer to see what's really happening between
the input, the hidden layer and the output.
So this is a very simplified neural network consisting of only one neuro.
So we call this a computing node because it is in the hidden layer and x1 extra extra are actually the
inputs.
So those are the pixels we are going to plug in to this neural network.
Next, we have weights now every input will be assigned a way this way contributes to how active this
neuron is, because what happens is when you try to recognize an image using neural network is that
some of the hidden layers will be disconnected.
Some of the dealers will be activated and connected.
Some of them will have a huge impact on the final calculation.
Some of them will have a very low impact on the final calculation.
So these weights here and since they are connected to the inputs, are going to specify how strong,
how effective this note is to the whole network.
So this is what this next we have effects, which is the activation function.
Now, an activation function is going to take some relationship between the input and the weight, and
it's going to decide what number to output here or it's going to output any number at all.
So there is a different type of activation functions.
We are going to learn some of them so that we can see how we can apply it.
But for now, for simplification, think about it as a function that decides if a number is larger than
zero or not.
So you can think about it like that.
Then we have whi, which is the output.
So the output is actually the final decision that we are classifying an image.
So the Y here will decide if this image is a dog or not.
Is it a cat or not.
Is it a circle or not.
So this final output here is going to give you the final decision based on all the calculation that
happens in this neuron.
Now, what is the relationship between all of these?
It can be written like this, so don't really freak out.
It's very simple mathematical relationship.
Forget about this for now.
You can see that Y is equal to X one times W one plus X2 times W two plus X three times W three.
OK, then we take all of these and check if it's larger than zero or not.
Remember I told you that, for example, this activation function is only to decide if this whole relationship,
this whole mathematic behind here is larger than zero or not.
So this is why we say f all of this relationship so that if the number here is larger than zero, this
Y is going to be one.
If this is less than zero or equal to zero, this Y is going to be zero.
So it's like just calculating this larger or equal than zero relationship.
Now, the next lecture, we're going to crunch some numbers here and see some examples.