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Explain the different layers of CNN.

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There are four layered concepts that we should understand in CNN (Convolutional Neural Network):

1. Convolution

This layer comprises of a set of independent filters. All these filters are initialized randomly. These filters then become our parameters which will be learned by the network subsequently.

2. ReLU

The ReLu layer is used with the convolutional layer.

3. Pooling

It reduces the spatial size of the representation to lower the number of parameters and computation in the network. This layer operates on each feature map independently.

4.  Full Collectedness

Neurons in a completely connected layer have complete connections to all activations in the previous layer, as seen in regular Neural Networks. Their activations can be easily computed with a matrix multiplication followed by a bias offset.

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