Compilers for Machine Learning (Spring 2027)
Comments
Ranked by a public rubric, not by votes: what a comment adds counts, not who wrote it. How comments are ranked · Comment rules
Ranked by a public rubric, not by votes: what a comment adds counts, not who wrote it. How comments are ranked · Comment rules
Summary
A draft Spring 2027 Carnegie Mellon course syllabus by George Hotz, "Compilers for Machine Learning," describes a one-semester, hands-on course in which students build an ML compiler from scratch, progressing from elementwise programs to training LLMs on GPUs.