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In a neural network, the term "hidden layers" refers to the layers that come between the input layer and the output layer. Hidden layers are called "hidden" because they are not directly connected to the input or the final output of the network. These layers play a crucial role in capturing complex, non-linear relationships in the data and extracting high-level features.

A typical feedforward neural network consists of the following layers:

1. Input Layer: This layer takes the raw input data and passes it to the first hidden layer. The number of neurons in the input layer is determined by the dimensionality of the input data.

2. Hidden Layers: These are the layers that come between the input and output layers. A neural network can have one or multiple hidden layers. The number of neurons in each hidden layer and the activation functions used in these layers can be adjusted based on the complexity of the problem and the architecture of the network.

3. Output Layer: The output layer produces the final result of the neural network's computation. The number of neurons in the output layer depends on the specific task the network is designed for. For example, in a binary classification problem, the output layer may have one neuron with a sigmoid activation function, while in a multi-class classification problem, it may have multiple neurons with softmax activation.

The architecture of the hidden layers can vary widely depending on the problem and the design choices made. Each hidden layer applies a linear transformation followed by a non-linear activation function to the data. Common activation functions include ReLU (Rectified Linear Unit), sigmoid, and hyperbolic tangent (tanh).

Table 3827. Hidden layer in deep learning neural network.

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