I recently completed the 2025 version of this course (video + most assignments, skipping some of the most costly part of the tasks). That's quite something. There is a lot going on in the first two assignments which required a ton of thinking and debugging. Despite having a decent foundation in deep learning, it took me several months to finish it using bits of my after-work hours and weekends. (I am not a model part-time student by any means, and sometimes I didn't get to work on this for days, but it could have been much worse.) Hard to imagine how enrolled Stanford students manage to submit assignments in two week cadence.
Coming back to the course, kudos to the course staff, including professors and TAs. The obviously put a ton of thought in designing the course, putting together those slides that contain the latest updates of the field, and preparing the wonderful assignments. You get to create a real LM and explore other important parts of LLM pipeline from small building blocks and validate them, validate each step, and see for yourself how everything comes together. You can really feel a sense of achievement after completing the assignments.
That said, while the staff obviously put a lot of effort into making this accessible to everyone, I wish they made a bit more effort in clarifying the environment requirement. Their harness works best on a Linux environment with NVIDIA GPU, which may be taken for granted for researchers but rare for home computer setup. Their setup also expects specific CUDA versions and/or architectecture. For following at home, the next best setup is Windows with WSL2 + NVIDIA GPU, plus leased GPUs on various platforms, none of which is exactly trivial (or cheap, for that matter). It would be nice if the staff could put together a bit more guidance in that area, especially how someone without any compatible GPU can make the most out of the course. (One thing I learned is that if you use Mac OS and are not careful about memory analysis, your python code could freeze and force reboot your machine).
Coming back to the course, kudos to the course staff, including professors and TAs. The obviously put a ton of thought in designing the course, putting together those slides that contain the latest updates of the field, and preparing the wonderful assignments. You get to create a real LM and explore other important parts of LLM pipeline from small building blocks and validate them, validate each step, and see for yourself how everything comes together. You can really feel a sense of achievement after completing the assignments.
That said, while the staff obviously put a lot of effort into making this accessible to everyone, I wish they made a bit more effort in clarifying the environment requirement. Their harness works best on a Linux environment with NVIDIA GPU, which may be taken for granted for researchers but rare for home computer setup. Their setup also expects specific CUDA versions and/or architectecture. For following at home, the next best setup is Windows with WSL2 + NVIDIA GPU, plus leased GPUs on various platforms, none of which is exactly trivial (or cheap, for that matter). It would be nice if the staff could put together a bit more guidance in that area, especially how someone without any compatible GPU can make the most out of the course. (One thing I learned is that if you use Mac OS and are not careful about memory analysis, your python code could freeze and force reboot your machine).