Summary

In this chapter, we have extensively explored recurrent neural architectures. First, we learned about various RNN types: one-to-many, many-to-many, and so on. We then delved into the history and evolution of RNN architectures. From here, we looked at simple RNNs, LSTMs, and GRUs to bidirectional, multi-dimensional, and stacked models. We also inspected what each of these individual architectures looked like and what was novel about them.

Next, we performed two hands-on exercises on a many-to-one sequence classification task based on sentiment analysis. Using PyTorch, we trained a unidirectional RNN model, followed by a bidirectional LSTM model with dropout on the IMDb movie reviews dataset. In the first exercise, we manually loaded and processed the data. In the second exercise, using PyTorch's torchtext module, we demonstrated how to load the dataset and process the text data, including vocabulary generation, efficiently and concisely.

In the final section of this...