Summary: Recurrent Neural Networks
Review what we've learned in this chapter.
In this chapter, we looked at RNNs, which are different from conventional feed-forward neural networks and more powerful in terms of solving temporal tasks. Specifically, we discussed how to arrive at an RNN from a feed-forward neural network-type structure. We assumed a sequence of inputs and outputs and designed a computational graph that can represent the sequence of inputs and outputs.
This computational graph resulted in a series of copies of functions that we applied to each individual input-output tuple in the sequence. Then, by generalizing this model to any given single time step