Welcome to this course on Generative AI with Large Language Models. Large Language Models, or LLMs, are a very exciting technology. But despite all the buzz and hype, one of the things that's still underestimated by many people is their power as a developer too. Specifically, there are many machine learning and AI applications that used to take me many months to build, that you can now build in days or maybe even small numbers of weeks. This course will take a deep dive with you into how LLM technology actually works, including going through many of the technical details like model training, instruction tuning, fine tuning, the Generative AI project lifecycle framework to help you plan and execute your projects, and so on. Generative AI and LLMs specifically are a general purpose technology. That means that, similar to other general purpose technologies like deep learning and electricity, it's useful not just for a single application, but for a lot of different applications that stand many corners of the economy. And so, similar to the rise of deep learning that started maybe 15 years ago or so, there's a lot of important work that lies ahead of us that needs to be done over many years by many people, I hope including you, to identify use cases and build specific applications. And because a lot of this technology is so new and so few people really know how to use them, many companies are also right now scrambling to try to find and hire people that actually know how to build applications with LLMs. And I hope that this course will also help you, if you wish, better position yourself to get one of those jobs. I'm thrilled to bring to you this course, along with a group of fantastic instructors from the AWS team, Antje Baaf, Mike Chambers, Shelby Eigenbrode, who are here with me today, as well as a fourth instructor, Chris Fragley, who'll be presenting SolarLabs. Antje and Mike are both generative AI developer advocates, and Shelby and Chris are both generative AI solutions architects. So all of them have a lot of experience helping many different companies build many, many creative applications using LLMs, and I look forward to all of them sharing this rich hands-on experience in this course. And to develop the content for this course with input from many industry experts and applied scientists at Amazon, AWS, Hugging Face, and many top universities around the world. Antje, perhaps you can say a bit more about this course. Sure, thanks, Andrew. It's a pleasure to work with you again on this course and the exciting area of generative AI. With this course on generative AI with large language models, we've created a series of lessons meant for AI enthusiasts, engineers, or data scientists looking to learn the technical foundations of how LLMs work, as well as the best practices behind training, tuning, and deploying them. In terms of prerequisites, we assume you are already familiar with Python programming and at least basic data science and machine learning concepts. If you have some experience with either PyTorch or TensorFlow, that should be enough. In this course, you will explore in detail the steps that make up a typical generative AI project lifecycle, from scoping the problem and selecting a language model to optimizing a model for deployment and integrating into your applications. This course covers all of the topics, not just at a shallow level, but will take the time to make sure you come away with a deep technical understanding of all of these technologies and be well positioned to really know what you're doing when you build your own generative AI projects. Mike, why don't you tell us a little bit more details about what the learners will see in each week? Absolutely, Antje, thank you. So, in week one, you will examine the transformer architecture that powers large language models, explore how these models are trained, and understand the compute resources required to develop these powerful LLMs. You'll also learn about a technique called in-context learning, how to guide the model to output at inference time with prompt engineering, and how to tune the most important generation parameters of LLMs for tuning your model output. In week two, you'll explore options for adapting pre-trained models to specific tasks and datasets via a process called instruction fine-tuning. Then in week three, you'll see how to align the output of language models with human values in order to increase helpfulness and decrease potential harm and toxicity. But we don't stop at the theory. Each week includes a hands-on lab where you'll be able to try out these techniques for yourself in an AWS environment that includes all the resources you need for working with large models at no cost to you. Shelby, can you tell us a little bit more about the hands-on labs? Sure thing, Mike. In the first hands-on lab, you'll construct and compare different prompts and inputs for a given generative task, in this case, dialogue summarization. You'll also explore different inference parameters and sampling strategies to gain intuition on how to further improve the generative model responses. In the second hands-on lab, you'll fine-tune an existing large language model from Hugging Face, a popular open source model hub. You'll play with both full fine-tuning and parameter efficient fine-tuning, or PEFT for short, and you'll see how PEFT lets you make your workflow much more efficient. In the third lab, you get hands-on with reinforcement learning from human feedback, or RLHF. You'll build a reward model classifier to label model responses as either toxic or not toxic. And don't worry if you don't understand all these terms and concepts just yet. You'll dive into each of these topics in much more detail throughout this course. So I'm thrilled to have Antje, Mike, Shelby, as well as Tris presenting this course to you that takes a deep technical dive into LLMs. You come away from this course having practiced with many different concrete code examples for how to build or use LLMs, and I'm sure that many of the code snippets will end up being directly useful in your own work. I hope you enjoy the course and use what you learn to build some really exciting applications. So with that, let's go on to the next video where we start diving into how LLMs are being used to build applications.