Generative AI with Large Language Models

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(25 customer reviews)
Product is rated as #1 in category Algorithms

In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in … Enroll for free.

25 reviews for Generative AI with Large Language Models

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  1. Bernard Leong

    Great overview of how to build, fine-tune and enhance the LLM model and how it can connect to the applications layer.

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  2. Tarun Kumar Chawdhury

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

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  3. Ritvik Iyer

    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.

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  4. Choy-Hsien Lin

    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.

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  5. Michael Rocha

    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.

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  6. Nathan Breitsch

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

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  7. Christopher L Rogers

    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!

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  8. Shafkat Rahman

    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.

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  9. Arman Tshitoyan

    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.

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  10. Cornelius Griggs

    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.

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  11. Ryan Tabar

    Enlightening, Inspirational, and Riveting. Three words I wouldn’t typically associate with online learning courses. Throughout the weeks of immersive lessons, the once mysterious structure of LLMs became clear and comprehensible to me. I was astonished to discover that the phrase “Attention Is All You Need” originates from a 2017 paper authored by individuals from Google and the University of Toronto. This transformative paper introduces a transformer architecture that employs an attention mechanism to assess the interconnections between words within a sentence.
    While the course doesn’t delve excessively into complex mathematics, a basic grasp of matrix arithmetic and calculus can be beneficial in understanding the foundational mechanisms utilized for modeling and training Artificial Neural Networks. Yet, it is important to note that this course is designed to cater to learners of all levels, whether they are novices or well-acquainted experts in the STEM field. While no coding is required during the labs, a working knowledge of the Python programming language can prove advantageous, as it serves as the primary tool for developing and analyzing these LLMs.
    However, the course goes beyond the surface and delves into the full Generative A.I. lifecycle, which seeks to manage and direct the desired outcomes for specific applications. Concepts like PEFT (Parameter Efficient Fine Tuning) are thoroughly explored, enabling a significant reduction in the number of tuned parameters in the model, thereby leading to a smaller memory footprint and less computational strain.
    Considering the emergence of this transformative and generation-defining technology, I firmly believe that it is crucial for individuals from various fields to grasp the intricacies of generative A.I. This collective understanding will enable us to wield its potential responsibly and ensure that it ultimately benefits humanity.
    Furthermore, the course emphasizes the paramount importance of governance, regulation, and policy guidance in the training of these models. In its concluding segments, the course takes a serious approach to exploring strategies for mitigating unwanted A.I. behaviors, such as toxicity and misinformation, fostering a responsible and ethical approach to A.I. development.

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  12. James Delmerico

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

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  13. Madhu Sudan Vashist

    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.

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  14. Gourav Kumar Sapra

    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.

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  15. Yury Kashnitsky

    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.

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  16. Wenjing Liu

    It is very informative and practical. It can really help machine learning engineers to understand and fine tune their own LLMs to adapt to various application scenarios.

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  17. INDRAJIT SINGH

    Wanted some more technical depth

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  18. Matt

    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).

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  19. Kamil Bagautdinov

    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!

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  20. vivek chand

    This course has been an incredible journey, guiding me through the fascinating world of LLMs, from the fundamentals of the Transformers architecture to tackling complex computational challenges through fine-tuning. I also gained valuable insights into reinforcement learning from human feedback, which was both intriguing and intellectually rewarding.
    What truly impressed me was the course’s comprehensive approach, featuring thoughtfully crafted quizzes and hands-on labs. The structured nature of the materials allowed me to dive deep into each week’s content, and the example workbooks provided a practical bridge between theory and application. It’s evident that the creators of this course put significant effort into ensuring a meaningful and engaging learning experience.

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  21. Nithin Padayatti

    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.

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  22. Clifford Gong

    A great introductory course to LLM’s and generative AI. If you’re somewhat technical (i.e. know python and have some basic knowledge of data science, for example NLP) and you want to get up to speed on the hot new topic of generative ai, this is a great course for you. It has a a lot of information packed into 3 weeks. The focus of the course is on improving an existing LLM to fit your specific needs (as opposed to creating LLM’s from scratch). The lab assignments are done in AWS Sagemaker Jupyter notebooks. They’re very straight forward follow-along style assignments which go over the mechanics of how to do various things with the LLM. The instructors are articulate and make the subject matter very approachable. Overall I found this course to be very helpful to get from nearly no knowledge, to a point of where I have enough to start doing lots of experiments.

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  23. Michel Helal

    Another course from DeepLearning.AI that doesn’t disappoint. 🔥 Course kicks things off with some classics:🏗️ LLM Architecture🧐 Attention Mechanism🏋️ LLMS Training🚀 Applications . But it doesn’t stop there! It delves into what I believe is even more crucial for those of us in applied data science:💡 Fine-tuning strategies and their associated costs 💰💻 Viewing LLMS as pieces of software (which they really are)🔗 Integrating them into the rest of a tech company’s stack with the LangChain libraryPlus, the talented instructors share some cool tricks for solving complex tasks with LLMs using in-context learning 🤓 and Chain-of-thought prompting. 🧠Highly recommended! 👌😄

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  24. David Solis

    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

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  25. Anton Byelikov

    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

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