What function does the input layer serve in a neural network?

The input layer in a neural network plays a crucial role as the entry point for data. Its primary function is to receive data signals that represent the input features or characteristics of the data being analyzed. Each node in the input layer corresponds to a specific feature of the input data, whether it's pixel values of an image, words in a text, or other types of data points. This layer does not perform any processing on the data; instead, it simply forwards the received data to the next layer in the network for processing.

In contrast, the other options do not accurately describe the purpose of the input layer. The processing of final predictions is handled by the output layer, while scheduling training sessions and managing user interactions pertain more to the overall management and administrative aspects of a machine learning project rather than the specific function of layers within a neural network. Thus, the input layer's role as a data receiver is foundational to a neural network's architecture.