The lectures define many important concepts in easy to understand terms, but they rarely go into the details needed to implement these ideas. You definitely won't have any idea of the pitfalls involved in any project like this. All the coding is done in the labs for you. You won't have to debug anything or figure anything out, just press shift-enter. The labs should require quite a bit more input from the student so the student can have some confidence upon attempting to implement some of these techniques.

The labs did not require any code changes to complete and were similar to freely available notebooks.

Good overview of key topics, but the course isn't as practical as I would have hoped for those from a engineering background (i.e. want to implement concepts in code). The labs felt like I was just running code cells and I didn't get much of an opportunity of do much implementation from scratch which would have helped my learning.

It would have been better to have an opportunity to write the codes of the assignments by ourselves instead of having it already written by the instructor.

Pretty superficial coverage. Labs were over simplified - you were just executing someone else's pre-baked code.

AWS and DeepLearning.AI structured the course into three comprehensive modules. In Week 1, learners explore the use cases, project lifecycle, and model pre-training of LLMs, including hands-on labs to construct and compare different prompts for generative tasks. Week 2 emphasizes fine-tuning and evaluating large language models, introducing techniques like parameter-efficient fine-tuning (PEFT), Low-Rank Adaptation(LoRA), and quantization to optimize computing resources (QLoRA). Week 3 explores reinforcement learning and LLM-powered applications, teaching how to align models with human preferences and optimize them for deployment. ... The course's target audience is AI enthusiasts with a foundational understanding of machine learning and coding in Python. It offers a distinctive opportunity to deeply comprehend generative AI, learn state-of-the-art training, tuning, and deployment methods, and apply this knowledge to real-world scenarios. By the end of the course, it will equip learners to make informed decisions for their companies and quickly build working prototypes using LLMs. Key Takeaways - Comprehensive understanding of generative AI and LLMs. - Hands-on experience with training, fine-tuning, and deploying models. - Insights from industry experts and practitioners. - Practical applications and challenges of generative AI in business. - Suitable for individuals with prior Python experience and a fundamental understanding of machine learning concepts. Please see the complete review on LinkedIn https://www.linkedin.com/posts/dsolis_ai-artificialintelligence-machinelearning-activity-7100135915246804992-THLt

The "Generative AI with Large Language Models" course by Antje Barth, Chris Fregly, Shelbee Eigenbrode and Mike Chambers, offered by Amazon Web Services (AWS) in collaboration with DeepLearning.AI and Andrew Ng is a comprehensive deep dive into the world of LLMs covering the entire LLM project lifecycle including Model Selection, Model Pre-training, Model Fine Tuning, PEFT, Prompt Tuning, RLHF, Chain-of-thought, PAL, ReAct, LangChain, Model Optimization and Deployment architecture. It also includes a great introduction to the Transformer architecture, several references to research papers backing the concepts taught and additional links to materials that provide more detail on the subject matter. As a novice in the area of AI/ML and LLMs, I found the material to be accessible yet providing enough depth and optional references to allow me to go deeper into the areas that interested me. I strongly recommend this course to anyone who is interested in LLMs and in building applications using LLMs.

Very insightful, in depth and well explained course, that provides a solid explanation about the technical aspects, economical considerations and project lifecycle of AI LLM powered solutions

This course is a deep dive into the nitty-gritty of how large language models work. I've taken a few other courses on generative AI, and this one is by far the most comprehensive. It covers everything from the basics of LLMs to how to fine-tune them for specific tasks.

The course is jointly offered by Coursera and AWS, so you can tell that it's got a strong focus on real-world applications. There are a ton of hands-on labs that let you practice what you've learned, and the instructors are all experts in the field.

If you're serious about learning about LLMs, this is the course for you. It's not for beginners, but if you have a basic understanding of machine learning, you'll be able to follow along just fine.

Nice introductory course, but not highly practical in real-life applications. It would be great if there were a more advanced specialization that includes programming tasks and delves deeper into the mathematical aspects of the algorithms. Thanks!

Great course for some one who is new to fine tuning and alignment of Large Language Models. In my opinion this course is suited to someone who has already worked with LLM's and frameworks like Langchain and has an idea about prompt engineering and retrieval augmented generation and has some hands on experience with hugging face and its packages. The topics are explained very neatly and thoroughly but the labs lack hands on work (well we could try new code in the provided notebooks and aws environment, but there is no preset questions or coding tasks). That is the only drawback which I can say. This certificate will spice up ones CV and one can learn the working of LLM's.

The course I was looking for. Just in time. Thank you

Good course to learn and understand LLM and Generative AI. One thing I found missing is Exercise for students. There are labs but those are all 100% ready to use and understand labs. There should be hands on Exercise for students so they develop programs and submit their responses to complete this course.

I went through the course and didn't feel like I learned much. The material was fairly boring and I didn't feel that the instructors motivated the material well.

A very good course covering many different areas, from use cases, to the mathematical underpinnings and the societal impacts. And having the labs to actually get to play around with the algorithms.

Excellent, A lot of things covered. No words to describe how the complex topics explained in such a simple manner. One suggestion is to include more hands-on labs with different kind of tasks.

This is great course, just you need little very fast understanding of concepts. A unique opportunity to ramp up at very high speed your learning of LLM and extend the benefits.

Very good course, but it lacked a lot of detail regarding the code from the laboratories. It's not about needing a basic Python or PyTorch course, but neither is it about breezing over everything without stopping to explain those aspects of the laboratory that require it. Not everyone works at Amazon or Apple, and that's why we take this course.

Overall the course is very good. It feels easy as video after video rolls on with content, but the actual material is dense and needs some additional time to research (and maybe rewatch later).

The tutors are great, really competent, and also very diverse and inclusive. On the other hand, there is a lack of practical exercises and assignments where we can apply and test these knowledge areas by ourselves. The course materials, such as notebooks, are already provided, which makes it less engaging as we don't have the opportunity to think independently and receive feedback.

AWS billed me $710 for doing the course exercises, and I am not able to get Coursera to correct this. Shame on Coursera.

I can full-heartedly recommend the course. It's a short but pretty dense course that:

- teaches you basic concepts about LLMs. Now I won't confuse instruction fine-tuning with regular fine-tuning or prompt engineering with prompt tuning

- explains RLHF in detail, PEFT (including LORA), and other practical aspects of using LLMs in the wild

- reviews LLM application development, interaction with external applications, LangChain, etc.

- guides through the code to summarize dialogues, perform instruction fine-tuning with PEFT and detoxify summarization with RLHF

Maybe I only missed a lab in which I'd implement some real-world LLM-based application, otherwise, the theory will quickly be forgotten. Also, without debugging tips, I can't imagine building real-world LLM-backed applications. Still, looks like wishful thinking that LLM will get the prompt right.

By far one of the best course covering Generative AI that I have had the pleasure to experienced. The instruction was clear, concise, and thorough and well supported by the additonal readings and weekly quizzes. The hands-on labs were the icing on the cake, so to speak, and provided an opportunity to not only see everything in action but to experiment with the code to test other theories and methods for running the training scripts. I even found a possible modification to the week three lab and used ChatGPT to help me analyze it and write a modified routine which I was able to run and see a performance difference. Kudos to all the instructors and contributors from DeepLearning.ai, Coursera, and AWS for an amazing course. Well worth the effort and the cost!

Easily a five star course. You will get a combination of overview of advanced topics and in depth explanation of all necessary concepts. One of the best in this domain. Good work. Thank you teachers!

The content was engaging and offered great learning on how to train and fine-tune LLM models. I would advocate this course to any of us who is interested in learning more about Generative AI.