Supplemental Resources

My intent is for this course to be as self-contained as possible. Toward this goal, the pop quizzes and exam will only concern the content discussed during lectures. Likewise, the homework assignments will require you to apply the lecture content to solve problems, which involves significant programming. We expect students to already have a strong foundation in programming and machine learning. If you do not already know how to program in TensorFlow or PyTorch, you will need to pick it up as you go (as we will not have time in class to teach such). These incredibly popular and useful frameworks make machine learning work significantly easier, so your experience with them will serve you well beyond this course. The research project, by design, will require you to take initiative to learn about NLP beyond what is covered in class, and to make a novel contribution.

There is a wealth of resources available online to help you fill in any gaps and to supplement your knowledge. It can be incredibly fruitful to read/hear others discuss the same content that I cover in lecture, as it not only reiterates what you already know, but it can provide an additional perspective to help you master the material. Thus, I highly encourage everyone to consider the following, phenomenal resources:

BOOKS

NLP

MACHINE LEARNING

MATH


COURSES (MOST HAVE VIDEOS)

NLP

MACHINE LEARNING

ONE-OFF

MATH


TRANSFORMERS

BLOGS/WRITE-UPS

Jay Alammar’s famous blog posts:

YOUTUBE

CODE


OTHER

PyTorch: