Instructor: Asa Ben-Hur
Email: asa\uff20colostate.edu<\/p>\n\n\n\n
In this course you will learn the theory and practice of deep learning, from linear and logistic regression to the transformer architecture behind modern language models. Nothing stays hidden: every component is implemented in full, in notebooks you can read, run and take apart. You will train a small language model from scratch and watch fluent English emerge from it, adapt a pretrained model to a new task, and develop a sense of which architecture a problem actually calls for.<\/p>\n\n\n\n
In this course you will learn to:<\/p>\n\n\n\n
Along the way, you will also learn to:<\/p>\n\n\n\n
For implementation, we will be using Python<\/a>. Previous experience with Python and the NumPy package is helpful. On Canvas, you will find detailed instructions on how to install the necessary packages for this course.<\/p>\n\n\n\n Note there is strong overlap between CS 545 and CS 445 in terms of material; CS 545 will go into more depth and assumes greater mathematical maturity.<\/p>\n\n\n\n Class meetings will be a combination of lectures by the instructor and discussions of student questions. All questions are welcome, no matter how simple you think they are; it is always true that someone else has a similar question. You can ask questions in class or via the class Teams group.<\/p>\n\n\n\n There is no textbook. All course materials will be provided as Jupyter notebooks available through the course’s GitHub repository<\/a>. The course materials are based on the textbook Dive into Deep Learning.<\/a> but are being significantly updated to reflect current practices in deep learning.<\/p>\n\n\n\n During this course we will cover the following topics:<\/p>\n\n\nCS 545 compared to CS 445<\/h2>\n\n\n\n
Please ask questions!<\/h2>\n\n\n\n
Textbook<\/h2>\n\n\n\n
Topics<\/h2>\n\n\n\n