Which layer of a neural network is responsible for providing the final output of the model?

The layer responsible for providing the final output of a neural network is known as the output layer. This layer receives the processed information from the hidden layers of the network, which have performed various transformations and computations based on the input data.

The output layer's primary function is to interpret this processed information and produce a result, whether that be a classification label, a probability, or a numerical value, depending on the specific task the neural network is designed to perform. In a classification task, for example, the output layer typically uses an activation function, such as softmax, to generate probabilities for various classes, ensuring that the output sums to one.

Understanding the role of the output layer is crucial in neural network design because it directly determines how the predictions made by the network are presented and interpreted. This is fundamental for assessing the model's performance against actual results during evaluation phases. The other layers, such as the input and hidden layers, play supportive roles in the data processing pipeline but do not provide the final output that encapsulates the network's predictions.