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Now, let us look at another CNN architecture.

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Google, it was the winner of 2014 challenge.

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Google and had one new concept and the content was often Inception module interprete module.

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Looks something like this.

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That input to the Inception module is given to four different layers.

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Three of these layers are convolutional layers.

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And the fourth one is a Max Buhler.

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If you look at these convolutional layers, these are also one by one cardinal.

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That is the window is of the size of a single pixel.

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Usually we have been using conventional layers with two by two or three by two window.

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But in the Inception module, you can see that windows or space one by one are used.

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This one, this represents distrait.

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So it has a straight one, the output of these two convolutional layers.

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And this match pulling layer then went into three different convolutional layers.

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The output of these forward was then put into a depth concatenated.

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Will not be discussing that concatenated here.

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This whole thing is called an inception module.

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And the actual architecture of Google, it was something like this input of images right through here.

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Then there was a convolutional layer, IMAX buhler, a local response normally to convolutional layers

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and so on.

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All of these are stacked inception modules.

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So this is the Inception layer.

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This is Inception layer.

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So many of these inception layers, the output of these goes on to another set of inception layers.

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And then we finally have a fully connected neural network.

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So if we look at it, it is a very complex network, although it had very few training parameters for

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most of the working professionals, are student working in the field of data science and machine learning,

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creating such architectures on their machines is not possible.

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So one of the good things that is coming with our Guide US library is that we are able to use these

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pre trained models for our problem.

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These architectures were created to solve one particular problem.

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But Tenent allow us to use these architectures for other problems.

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Also, let us see how.