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in the last couple of videos we saw that convolutional neural networks create features to represent parts of images and then later combine those features together to understand larger and larger parts of the image however we also saw that if the network is supposed to process a 1000 by 1000 image it needs 500 layers with that many layers there could be problems with diminishing gradients where the network is simply unable to learn what the proper weights are in the beginning of the network because there are just way too many layers also even though convolutional filters are fast and use few weights with that many layers there could be problems with memory or execution time as well how do we reduce the number of layers needed in convolutional neural networks one way is pooling pooling is a method to dramatically reduce the size of the intermediate features if we reduce the size of the intermediate features then we need less layers in order to cover the whole input or to reduce the inpu...
