What role do hidden layers play in neural networks?

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Hidden layers are essential components of neural networks primarily responsible for processing and learning complex representations of data. They sit between the input layer and the output layer and consist of multiple neurons that transform the input data through weighted connections and activation functions. Each hidden layer can capture different features and patterns in the input data, allowing the network to learn hierarchical representations. This ability to model intricate relationships in data is what enables neural networks to perform well on a variety of tasks, such as image recognition, natural language processing, and more.

The other options describe elements that are either not the primary function of hidden layers or pertain to different parts of the neural network. The final output layers deliver the model's predictions, while input layers are responsible for receiving data. Storing parameters is also not a function of hidden layers; rather, parameters (weights and biases) are associated with all layers collectively, influencing how data is transformed as it moves through the network. The correct understanding of the role of hidden layers clarifies their importance in the learning process of neural networks.

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