Hello and welcome to the Street Odille on Convolutional Neural Network.
Let us begin a convolutional neural network.
The type of neural network that is mostly used for the image processing problems.
The advancements in computer vision and deep learning has been constructed and preffered with convolutional
neural network.
There are multiple applications of convolutional neural network, like a major recognition, video recognition,
image analysis, image classification, Milioti creation recommendations, esteems natural language
processing.
And this list goes on and on.
To understand the convolutional neural network, we have to understand the different layers in that.
These are the five main layers in convolutional neural network input layer convolution layer at a layer,
pooling layer and fully connected layer.
Let us understand these five layers, one by one in detail.
This is the pictorial representation of delayers in convolutional neural network.
As you can see here, first VLT input layer, then convolution, plus add a little layer after that
fulling layer.
Then again, convolution pessary, Lou.
And pulling the combination of these three layers, that is convolution plus RELU plus pulling layer
is collected in layers.
After that, we have to flatten the data and provide the input to fully connected layer to the output
of fully connected layer.
We have to apply activation function here.
We have to apply softmax or sigmoid activation function.
As of now, just remember the name of layers.
Nothing is.
So this is the first layer in convolutional neural network that is input layer, the input layer the
horse, the raw input of image with very height and depth.
The convolution layer computes the output volume, and to do that, it calculates the dot product between
filter and image.
Let us understand the convolution layer in detail.
The convolution layer is the first layer to extract the features from an input image.
It prevents the relationship between pig cells by learning the image features convolution is a mathematical
operation that takes to input flat input is the image matrix and the second input is built.
So theoretically, this is all about the convolution layer.
Let us understand the mathematical operation.
To simplify the calculations, we take an image of the save for bifold.
And after that, we help filter to by two.
After the multiplication of image and filter, we will have the output of essays three by three.
Let us understand these.
First, we will apply this filter at the cordon it off this image.
Now multiply one into one one zero into one zero zero in two zero zero and one two one one.
So one plus one two after that, move this filter towards the right.
No, again, same calculations, one into one one zero in two zero zero zero and two one zero and one
in two zero zero.
Add all these values that these one.
In the same way, we help to do all the calculations like these.
And at the end, we will have the output of size three by three.
So this is how the convolution layer works.
If the input image is a squared sized image and input length is N garner, length is key.
Then output length of that image.
D and minus K plus one.
And if the input image is not this side's image, then output, height of that image is equal to input
height minus col, height plus vun and output width is equal to input with minus Cardinal Vade plus
one.
Bill, now in this story, you'll be held under two two CNN layers that these input layer and convolution
layer, we will understand the remaining three layers in next tutorial.
Till then, happy learning.