CNN works primarily by stacking the convolution and pooling layers. The convolution layer uses linear combinations between surrounding pixels to find new hidden data. The pooling layer shrinks the feature map, lowering the learning resources required and preventing overfitting.
The classification result is obtained using the fully connected layer after the data size has been reduced sufficiently by repeatedly applying these layers. For better results, the loss between the acquired label and the actual label can be used to train the model using a gradient descent method or other optimizers.
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