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A hidden layer is one of the layers of a neural network located between the input layer and the output layer, and it is not directly observable either from the raw input data or from the model's final result. It is in these intermediate layers that most of the processing and extraction of increasingly abstract data representations takes place.
Each neuron in a hidden layer receives as input the values produced by the previous layer, combines them through a learned weight matrix and applies a non-linear activation function, producing a new set of values that will be passed to the next layer. By stacking multiple hidden layers, the network can build increasingly complex representations: in networks that process images, the first hidden layers tend to recognize simple patterns like edges and textures, while deeper layers recognize shapes and whole objects.
It is an element present in every neural network with more than one layer, from simple multilayer perceptrons to the deep architectures used in modern language models, where the number and size of hidden layers are among the most determining architectural choices for the model's overall capacity.
The term "hidden" describes the fact that these layers are not directly visible or interpretable from the outside, unlike the input, which corresponds to raw data, and the output, which corresponds to the produced result: the intermediate values computed in hidden layers do not have an immediately readable meaning to a human observer.
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