Notebook: Character-Level RNN

Now you have all the information you need to implement an RNN of our own. The next few videos will be all about character-level text prediction with an LSTM!

It's suggested that you open the notebook in a new, working tab and continue working on it as you go through the instructional videos in this tab. This way you can toggle between learning new skills and coding/applying new skills.

To open this notebook, go to our notebook repo (available from here on Github ) and open the notebook Character_Level_RNN_Exercise.ipynb in the recurrent-neural-networks > char-rnn folder. You can either download the repository with git clone https://github.com/udacity/deep-learning-v2-pytorch.git , or download it as an archive file from this link .

Instructions

• Load in text data
• Pre-process that data, encoding characters as integers and creating one-hot input vectors
• Define an RNN that predicts the next character when given an input sequence
• Train the RNN and use it to generate new text

This is a self-assessed lab. If you need any help or want to check your answers, feel free to check out the solutions notebook in the same folder, or by clicking here .

Note about GPUs

In this notebook, you'll find training these networks is much faster if you use a GPU. However, you can still complete the exercises without a GPU. If you can't use a local GPU, we suggest you use cloud platforms such as AWS , GCP , and FloydHub to train your networks on a GPU.