Its good!

Very Good

Thank You

Loved it!

Awesome!

Amazing!

helpful

Perfect

thanks

superb

Brutal

Greate

Super

great

شكرا

good

nice

good

good

good

All the instructors were good and delivery was mostly excellent, however, the course was a bit too short can be improved in several ways. There were very few quizes in the video lectures and the ones that were present, were too easy or obvious (does not require much thinking). There should be good, quality quizes in most video lessons similar to the OG ML course by Andrew Ng. The inline quizes in videos help "reinforce" the learning in humans. This is proven by the research yet to be carried out :D Another aspect that I did not like was the jupyter notebooks to run excercises, all solutions were already provided and it does not help in learning the concepts if all we have to do is to press Shift+Enter and merely observe code and results. Actual learning requires some trail and error as part of the exercises, once again the OG ML course by Andrew Ng did a good job of accomplishing this with Octave exercises.

A delightful and very up-to-date (most of the references have been published in the last 2 years) overview of LLMs with hands-on lab sessions in Python. Prompt engineering, zero/one/few-shot inference, instruction fine tuning (FT), parameter-efficient FT (PEFT), Low-rank Adaptation (LoRA), RL from human feedback, program-aided language (PAL) models, retrieval augmented generation (RAG), etc, etc. In short, everything you need to know about the state-of-the-art in LLMs in 2023. There are a couple of things that disappointed me though. The first one is that, unlike other Coursera courses, there isn't any discussion forum to interchange ideas with other students or post questions. The second one is that there isn't any clear contact (either from the course's intructors or from Coursera) to ask questions regarding problems with the AWS platform when working on the labs.

Good entry-level course in general. Thanks to the course team for bringing us one of a few online courses on this timely topic.

I really like the lab sessions. Although it can be further improved by adding some exercises, like writing the code for the whole LLM task.

Proximal Policy Optimization lecture by Dr. Ehsan Kamalinejad is fantastic. It helps me real the PPO paper with both quantitative and intuitive understanding. In comparison, the sections of some important LLM architectures, such as the Transformer and InstructGPT, is a bit too much intuitive.

The final week is way too packed. Students need to know more than just names and a short intro of new LLM techniques or architectures. It would be better to have separate Lab for each topic (such as PTQ, RAG, etc.) for learners to REALLY understand what's going on.

I have to admit, I was a bit unsure at first if the course content would be able to navigate around all the hype on generative AI and LLM's. I was pleasantly surprised to find that the topics are based on scientific research and offer a broad perspective on the present issues in this kind of AI project. If I had to compare this course to the others from DeepLearning.AI, I'd say the only thing missing is the high-quality, problem-based, challenging hands-on practices in Jupyter Notebooks. Most of the practices are ready to go, you just have to run the code, which I don't think is ideal. But overall, it's still a great course to recommend!

Good overview of the different stages of developing an LLM application. I felt it gave me enough knowledge to be able to understand the current research and applications that are being developed for large-language models. The course gives you links to relevant papers that you can read for more in-depth coverage on how some of the latest LLMs are constructed and trained. I would have liked the labs to be more hands on. You basically run pre-built lab notebooks that use existing model implementations from widely available libraries like HuggingFace. There was no requirement to write any code.