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Convolutional neural networks create features to represent parts of images and combine them to understand larger parts. Processing a 1000x1000 image with 500 layers leads to diminishing gradients and learning problems. To reduce the number of layers, pooling can be used to decrease the size of intermediate features, requiring fewer layers to cover the input. Pooling helps in reducing memory usage and execution time in convolutional neural networks.