A layer in a neural network is similar to a layer in an onion. Each layer is built on top of the previous one, adding more complexity and functionality. The first layer is the input layer, which takes in data from the outside world. The next layers are called hidden layers because they perform calculations that are not visible to outside observers. These layers transform the input data into something that can be used by the final output layer. The output layer then produces results that can be used by decision-makers or other systems.

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