Summary

In this chapter, we worked on two NLP projects: sentiment analysis and text generation using RNNs. We started with a detailed explanation of the recurrent mechanism and different RNN structures for different forms of input and output sequences. You also learned how LSTM improves vanilla RNNs. Finally, as a bonus section, we covered the Transformer, a recent state-of-the-art sequential learning model.

In the next chapter, we will focus on the third type of machine learning problem: reinforcement learning. You will learn how the reinforcement learning model learns by interacting with the environment to reach the learning goal.