Now we are going to start with conversational neural networks.
And in this section we are going to understand some of the building blocks of CNN is first of all let
us understand the motivation behind CNN.
What is it that CNN do better than a normal artificial neural network.
CNN's are mostly used for problems like image recognition or speech recognition.
The reason for this is that CNN is outperform normal artificial neural networks in these types of problems.
In fact the accuracy of some CNN models and image recognition is even better than humans
let us try to see the limitation in a normal artificial neural network.
As we understand an artificial neural network gets the data of each pixel as input into the first layer
of neurons.
So if you have a sixteen by sixteen pixel image and I want to find what is in that image I will feed
the information of all these 256 pixels into the first layer but here's the problem if you see the pixels
randomly with no order.
Can you identify what this image is.
For example in the slide I have a image of our favorite video game character module.
It's a 16 by 16 picks element
to demonstrate how our neural network sees this image.
I have covered all of the pixels with blue squares and have revealed 25 random pixels in each of the
four images.
This is similar to what a neural network sees.
So if a neural network sees this first image do you think it will be able to identify the character
in this image or the second image.
Same goes with third and fourth image.
The point that I am trying to make here is we are not considering the effect of neighboring pixels if
we randomly big pixels.
We do not understand what is the image behind it.
If we consider the order of pixels only then we are able to identify what is the object in that image.
So identifying the object by looking at such images is a very difficult task because this is not how
the human brain works.
We do not look at individual points or pixels.
We can recognize pattern in group of points or pixels in fact served in our visual cortex respond to
different patterns.
Some respond to horizontal lines.
Some respond to vertical lines.
Some respond to other complex patterns.
The output of the lower level neurons is then processed by higher level neurons to identify object in
a visual field convolution all neural networks are inspired from this concept.
In CNS instead of looking at each individual pixel we look at a group of pixel
if we look at a group of pixels.
We are more likely to pick up different features of the objects in the image and once we know the features
it is more likely that we can predict the object in the image.
So in this light you can see that by using a window on the image or by looking at a group of pixels
we can identify certain features so you can see modules airhead in the first image in this image you
can see Mario's red collared shirt and probably a shoulder in the next image.
You find out some very important features of the character.
Eyes nose and a moustache the last window tells us that the character is wearing blue colored pants
on the legs with these features identified.
It is easier for our network to identify the object in our image.
So if you compare it with the previous slide in which we randomly showed you 25 pixels out of 256 pixels
you can easily see that in the second slide you can identify features because the group of pixels is
together as compared to the one in previous light where the pixels were randomly picked up.
So this is the main idea here.
Instead of looking at each individual pixel we will look at a group of pixels.
Now let's see how this is implemented.
So here does that image at the bottom.
This is the input image to our network.
Now on top of it we will have a convolution earlier layer this is the most important concept into units.
We have a convolution earlier a convolution layer comprises of neurons which take in information from
a group of pixels in the previous layer.
So in this first layer this neuron gets information stored in the pixels within this rectangular box.
Only this other neuron gets information from pixels of this rectangle only.
Similarly in the second convolution earlier information of all the neurons in this small rectangle that
is on the first layer is taken as input by this neuron
this architecture allows the network to concentrate on lower level features in the first layer and then
assemble these features into larger higher level features in the next hated layer and so on.
Now let us focus more on this window or the receptive feel of these neurons.
So this window is also known as the receptive field of that particular neuron.
This window has two dimensions height and weight in this image you can see that the height of the window.
We have taken is five pixels and Vertex also five pixels.
So we see that this is a five cross five window.
We can also have t cross three window order to cross three window or any such dimension.
Most commonly used dimensions are three cross three or five cross fire so this particular window the
information stored in all the pixels of this window will go in to one particular neuron in the upper
convolution earlier.