Modeling Temporal Data

This chapter covers recurrent neural networks (RNNs) and their applications in temporal data modeling. It discusses the architecture and mathematical formulation of RNNs, challenges with standard RNNs, and advanced architectures like LSTM and GRU networks. Attention mechanisms and sequence-to-sequence models are introduced, followed by training and optimization techniques for RNNs. Practical applications in language translation, sentiment analysis, and text generation are explored, with implementation examples and case studies using TensorFlow and PyTorch.