Description: Function composition in the context of recurrent neural networks (RNNs) refers to the process of combining two or more mathematical functions to create a new function that can model complex relationships in sequential data. In RNNs, this composition is fundamental as it allows the network to process information in time series, where the output of one function can influence the input of another. This approach is particularly useful for tasks such as natural language processing and time series prediction, where the meaning of an element may depend on the context provided by the preceding elements. Function composition in RNNs is achieved through layers of neurons that apply activation functions, such as sigmoid or hyperbolic tangent, to the inputs and outputs of the neurons at each time step. This enables the network to learn patterns and dependencies across data sequences, adjusting the weights of the connections between neurons during the training process. In summary, function composition in RNNs is a key mechanism that allows these networks to learn and generalize from sequential data, facilitating the modeling of complex phenomena in various applications.
