The purpose of the project is to install TensorFlow or PyTorch on your own machine and use it, within a Python program, to implement any project involving an NLP task and a neural network. For most tasks, I would prefer if you use some variation of a recurrent neural network, but I am open to other sorts of architectures if you think something else makes sense for your project. The task can be something that we covered (or will cover) in class, but it does not have to be. You should test your implementation on a dataset and report the results. (You are allowed to create a dataset, but it will be much easier if you find one.) Note that we are not covering either TensorFlow or PyTorch in class, so you will have to go through an on-line tutorial to figure out how to install and use the library.
Default project: Use one of the libraries to re-implement Project #1, for any one of the three datasets. If you choose dataset #2 or #3 (the ones for which I did not provide my test set), you will need to split the dataset into a smaller training set, probably a tuning set, and a test set. Use the training set along with the tuning set to experiment with architectures and hyperparameters, and only test the final version on the test set. If you choose dataset #1, keep the test set as is, and you'll probably want to split the training set into a training set and a tuning set
