Instructions:
See material on page 94 in Chapter 8: Recurrent Neural Networks (“Deep Learning with TensorFlow 2 and Keras” book. RNNs have been used extensively by the Natural Language Processing (NLP) community for various applications. One such application is to build language models. A language model is a model that allows us to predict the probability of a word in a text given previous words. For our example, we will train a character based RNN on the text of the children’s stories “Alice in Wonderland” and its sequel “Through the Looking Glass” by Lewis Carroll. We will train the network for 50 epochs, and at the end of every 10 epochs, we will generate a fixed size sequence of characters starting with a standard prefix. In the following example, we have used the prefix “Alice”, the name of the protagonist in our novels.
For the submission provide:
the code you used to build model.
the output after 50 epochs.
Two citations to be mentioned are:
1)Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson Education (US)
2)Gulli, A., Kapoor, A., & Pal, S. (2019). Deep Learning with TensorFlow 2 and Keras (2nd ed.). Packt Publishing.
