Summary

In this chapter, we learned about RNNs, a class of networks that is specialized for dealing with sequences such as natural language, time series, speech, and so on. Just like CNNs exploit the geometry of images, RNNs exploit the sequential structure of their inputs. We learned about the basic RNN cell, how it handles state from previous time steps, and how it suffers from vanishing and exploding gradients because of inherent problems with BPTT. We saw how these problems lead to the development of novel RNN cell architectures such as LSTM, GRU, and peephole LSTMs. We also learned about some simple ways to make your RNN more effective, such as making it bidirectional or stateful.

We then looked at different RNN topologies and how each topology is adapted to a particular set of problems. After a lot of theory, we finally saw examples of three of these topologies. We then focused on one of these topologies, called seq2seq, which first gained popularity in the machine translation...