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The pooling layer is important in the CNN as it performs a series of tasks which are as follows:
                 • Pooling reduces the spatial dimensions (height and width) of the feature map, which helps reduce the number
                 of parameters and computation in the network. This makes the model faster and more efficient.
                 • Even though the size is reduced, the most important features (like edges and patterns) are preserved, ensuring
                 that the network can still make accurate predictions based on the crucial information.
                 • Pooling helps make the model more robust to variations in the input image (e.g., slight changes in position,
                 rotation,  or  scale  of  objects  in  the  image).  Since  pooling  looks  at  local  regions,  it  makes  the  network  less
                 sensitive to small transformations.
                 • By reducing the spatial dimensions, pooling helps lower the risk of overfitting by simplifying the model,
                 ensuring it generalises well to new, unseen data.





                                                                                    Max


                                                                        Pooling




                                                                                    Sum






              A small difference in the input image will create a very similar pooled image.
              Fully Connected Layer


              This is the last and the final layer of the Convolutional Neural Network. After the features of the input image
              are extracted by the convolution layers and downsampled by the pooling layers, their output is a 3-dimensional
              matrix which is flattened into a vector of values. These values of the single vector represent a specific feature of a
              specific label and are redirected to fully connected layers to predict the final outputs of the network. This helps in
              classifying an image into a specific label based on the probability of the input being in a specific class. For example,
              the input image with a beak belongs to the category of a bird.
                                                                                               bird  p bird

                                                                                             sunset  p sunset

                                                                                              dog     p dog
                                                                                ...                   p
                                                                                               cat    cat
                             Convolution + Nonlinearity  Max pooling     Vec                  ...

                                     Convolution + Pooling layers        Fully connected layers Nx binary classification

                            Brainy Fact



                      The first image recognition CNN-AlexNet named after(creator-Alex Krizhevsky), won the 2012 ImageNet
                      Computer Vision contest with 85% accuracy.



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