Now, let us look at another CNN architecture.
Google, it was the winner of 2014 challenge.
Google and had one new concept and the content was often Inception module interprete module.
Looks something like this.
That input to the Inception module is given to four different layers.
Three of these layers are convolutional layers.
And the fourth one is a Max Buhler.
If you look at these convolutional layers, these are also one by one cardinal.
That is the window is of the size of a single pixel.
Usually we have been using conventional layers with two by two or three by two window.
But in the Inception module, you can see that windows or space one by one are used.
This one, this represents distrait.
So it has a straight one, the output of these two convolutional layers.
And this match pulling layer then went into three different convolutional layers.
The output of these forward was then put into a depth concatenated.
Will not be discussing that concatenated here.
This whole thing is called an inception module.
And the actual architecture of Google, it was something like this input of images right through here.
Then there was a convolutional layer, IMAX buhler, a local response normally to convolutional layers
and so on.
All of these are stacked inception modules.
So this is the Inception layer.
This is Inception layer.
So many of these inception layers, the output of these goes on to another set of inception layers.
And then we finally have a fully connected neural network.
So if we look at it, it is a very complex network, although it had very few training parameters for
most of the working professionals, are student working in the field of data science and machine learning,
creating such architectures on their machines is not possible.
So one of the good things that is coming with our Guide US library is that we are able to use these
pre trained models for our problem.
These architectures were created to solve one particular problem.
But Tenent allow us to use these architectures for other problems.
Also, let us see how.