Quiz #3 has been posted, and is due Monday at midnight! We’ll have covered all the material for this quiz by Thursday the 23rd.

I misspoke today in response to Garrett’s question about a vector-valued loss function (instead of a scalar loss function). If your loss (or any other) function is a vector of values, then computing the partial derivative of each of those values with respect to each of those inputs is called the Jacobian matrix. It’s normally…

I have pushed to the class github repo our code from today (see the file demo_autodiff.py.) Btw, I may have completely forgotten to mention the name of the awesome algorithm used to systematically back-compute the partial derivatives of the loss function with respect to all the model inputs. It is called autodiff. In a humorous…

I forgot to say that I would accept people’s XP cash cards today as a mid-semester cash out. So how about this: if you want to cash in your cards mid-semester, you can do so next Thursday (right after fall break). (Note that if you don’t turn them in mid-semester, there is no grade disadvantage:…

Quiz #2 has been posted, and is open-Python and timed at 60 minutes. So as not to rush anybody, I made it due on Oct. 15th instead of Oct. 10th. But we’ve already covered everything needed for the quiz.

In the class git repo.

On Tuesday the 7th, my office hours will be 1:30-3:30pm instead of the normal 12-2pm.

A student has just pointed out a couple errors in pytorch_practice.py, which are now fixed. If you’ve already git pulled (or copied the contents of that file some other way), then git pull again (or re-copy).

As promised, Homework #3 has been posted, and is due on October 17th at midnight. It is a play in two acts. Send questions! Also, I offer +5XP to anyone who finds a legit bug in my co-occurrence code or supporting programs and reports it! Only the first person who reports any specific bug gets…

I’ve pushed several files to the class repo, including two programs to help you visualize the embeddings in your corpus: interact_cooccur.py, which we played with in class on Tuesday, and visualize_cooccur.py, which can produce 2-d (and even 3-d) plots like this showing the embeddings in a reduced-dimensional space: The next homework assignment (coming soon) will…