In the context of neural networks, what does the hidden layer do?

The hidden layer in a neural network performs crucial processing of input signals to facilitate the creation of meaningful output. This layer is responsible for transforming the input data through a series of weighted connections and activation functions, which allows the network to learn complex patterns and relationships within the data. Each neuron in the hidden layer takes inputs from the previous layer, applies weights and biases, and then passes the result through an activation function to introduce non-linearity. This enables the neural network to model intricate functions and make predictions based on the learned representations.

While the final output of the model indeed comes from the last layer after the hidden layers, and while it may connect to the input layer, or even handle dimensionality in certain architectures, these aspects do not accurately describe the fundamental role of the hidden layer itself. Its primary purpose is to process inputs, extract features, and contribute to the overall predictive capability of the model.