What distinguishes the input layer from the output layer in a neural network?

The distinction between the input layer and the output layer in a neural network is primarily based on their roles in the information flow of the model. The input layer is responsible for receiving incoming data. It consists of neurons that take in various features from the dataset, which could be pixel values in an image, measurements in a table, or other signal types depending on the application. This layer is crucial as it serves as the point where the model begins processing the data.

In contrast, the output layer is where the final predictions or classifications are generated based on the processing that occurs in the hidden layers of the network. Each neuron in this layer typically corresponds to a particular outcome or class that the network is attempting to predict, whether it's a numerical value in regression tasks or class labels in classification tasks.

Understanding this clear functional difference between the layers helps in designing neural networks according to the requirements of the task at hand, ensuring that the input and output structures are appropriate for the specific data and the intended predictions.