Now, we have a good command over the basic learning mechanism of different types of RNNs, both simple and complex. We also know a bit about different sequence processing use cases, as well as different RNN architectures that permit us to model these sequences. Let's combine all of this knowledge and put it to use. Next up, we will test these different models on a hands-on task and see how each of them do.
We will explore the simple use case of building a character level language model, much like the autocorrect model almost everybody is familiar with, which is implemented on word processor applications for almost all devices. A key difference will be that we will train our RNN to derive a language model from Shakespeare's Hamlet. Hence, our network will take a sequence of characters from Shakespeare's Hamlet as input...
