If during revision you have questions about the coursework, feel free to post them on the Questions and answers page. In the same spirit of the rest of the course, please help out your students if you know the answer to their questions.
Some students have asked about the deadline for the class project. The deadline will be 4 weeks from now (the Monday after classes end) on 18 April.
As was mentioned in class, we put up a leaderboard where we ask you to submit the results you achieved on either project (classification scores for Dogs vs. Cats and samples or perplexity scores for the vocal synthesis task). Don’t wait until the end to do so, please put up intermediate results as well!
The final exam is in-class and scheduled for the last day of class, Thursday April 14, 2016, at the usual 9:30-11:30 class time.
You are not allowed to open up your phone or laptop (or any other means to connect to the internet) during the exam, but you are allowed to bring your own 8.5×11 cheat sheet (2-pager).
As a guide (but thing might be different), here are some exams from previous years.
As was briefly discussed in class and mentioned on the getting started page, you will need to split up your idea in subsequences in order to train RNNs. To do so, I just added a new transformer to Fuel that does this for you (should be merged soon, but you can check out the branch or copy-paste the code if you want to use it right now).
You can use it as follows:
from fuel.datasets.youtube_audio import YouTubeAudio from fuel.transformers.sequences import Window data = YouTubeAudio('XqaJ2Ol5cC4') stream = data.get_example_stream() sequence_size = 10 windows = Window(1, sequence_size, sequence_size, True, stream) for source, target in windows.get_epoch_iterator(): train(source, target)
If you want access to the GPU cluster for the class project and haven’t e-mailed me yet, please do so by Monday at the latest.
I added a page to the menu (under project description) to help people get started with loading and playing with the data for the class projects using Fuel, and a quick example of training a neural network on this data using Blocks. If you have any questions, feel free to leave a comment on the page.