What is the primary purpose of the output layer in a neural network?

The primary purpose of the output layer in a neural network is indeed to produce final predictions or results from the input data. This layer is the last component of the neural network architecture, where all the computations and transformations from previous layers culminate.

When an input is fed into the neural network, it passes through one or more hidden layers, where various features and patterns are extracted through activation functions and weights. Finally, the output layer processes the resulting representations to generate a specific output depending on the task at hand—this could be a classification label, a numerical value in regression tasks, or probabilities for various classes in multi-class scenarios. The structure of the output layer can vary according to the problem, such as using a softmax activation function for multi-class classification or a sigmoid function for binary classification.

By serving this crucial function, the output layer enables the network to fulfill its primary role of making predictions based on the learned representations from the input data.