In neural networks, what purpose do hidden layers serve?

Hidden layers in neural networks are crucial for performing intermediate computations. As data enters the neural network through the input layer, it must be processed before producing an output. The hidden layers are composed of neurons that apply various transformations and activations to the data, allowing the network to learn complex patterns and relationships within the input.

Each hidden layer takes the output from the previous layer, applies weights and biases, and then uses an activation function to introduce nonlinearities into the model. This process enables the network to capture intricate details and variations that are necessary for tasks such as classification, regression, or generation. By stacking multiple hidden layers, a network can model highly complex functions, which is why deep learning architectures are so powerful.

The other options, while they refer to aspects of neural networks, do not accurately describe the primary function of hidden layers. Transmitting input data relates more to the function of the input layer, optimizing model architecture is a broader consideration that involves various design decisions across all layers, and storing final results pertains to the output layer where predictions are made.