What is the function of hidden layers in a neural network?

Hidden layers in a neural network play a crucial role in processing and transforming inputs. These layers consist of neurons that apply activation functions to the weighted sums of their inputs, enabling the network to learn complex patterns and relationships within the data. The transformation that occurs in hidden layers allows the model to extract features at different levels of abstraction, which is essential for tasks such as image recognition or natural language processing.

By adjusting the weights and biases through training, neural networks can minimize errors in predictions. The presence of multiple hidden layers (often referred to as deep learning) can enhance the network's ability to model intricate functions, thereby improving the overall performance of the AI system on various tasks.

Other options reflect functions that do not accurately describe the role of hidden layers. For instance, storing data inputs pertains more to data management rather than processing, while collecting initial training data speaks to the data collection phase of model training, and managing output results relates to what the output layer does. Thus, the accurate depiction of hidden layers as processors and transformers of inputs underlines their fundamental importance in neural networks.