Quiz: Sequence Synthesis with GANs

In this chapter, we worked on GANs that directly generate sequential data, such as text and audio. Throughout this section, we learned the commonly used techniques of the NLP field, such as RNN and LSTM. We also learned some basic concepts of reinforcement learning (RL) and how it differs from supervised learning (such as SGD-based CNNs). We learned how to build a custom vocabulary from a collection of text so that we can train our own NLP models and learned how to train SeqGAN so that it can generate short English jokes. We also went through the concept of SEGAN to remove background noise and enhance the quality of speech audio.

Test your understanding

You can attempt the quiz below to test your understanding: