Welcome to Week 1 of the NVIDIA: Large Language Models and Generative AI Deployment course. This week, we will begin by introducing you to Large Language Models (LLMs) and explore their significance in Natural Language Processing (NLP). We will also demonstrate how LLMs are applied to various NLP tasks using HuggingFace. Next, we will dive into the concept of Generative AI models and their components. We’ll cover the importance of training data for LLMs and best practices for data cleaning. By the end of this week, you will have a solid understanding of LLMs, their applications, and the essential processes involved in training them.

Welcome to Week 2 of the NVIDIA: Large Language Models and Generative AI Deployment course. This week, we will cover the essentials of training and optimizing Large Language Models (LLMs). We will begin by exploring the various learning methods, including Few-shot, Zero-shot, Instruction Tuning, and Reinforcement Learning with Human Feedback (RLHF). Next, we will delve into loss functions used in LLMs and techniques for aligning models effectively. We will also cover evaluation metrics such as Perplexity and discuss the critical role of humans in evaluating LLMs. Additionally, we will examine the role of GPUs in training models and explore LLM fine-tuning techniques like Prompt Tuning and Parameter Efficient Fine-Tuning (PEFT). By the end of the week, you will have a solid understanding of how to train, optimize, and evaluate LLMs for real-world applications.

Welcome to Week 3 of the NVIDIA: Large Language Models and Generative AI Deployment course. This week, we will cover essential strategies for deploying Large Language Models (LLMs) in real-world applications. We will start by exploring various deployment strategies and how to choose the right one for different scenarios. Next, we will introduce ONNX as a tool for unifying the deep learning landscape, and demonstrate how to convert deep learning models using ONNX. We will also focus on monitoring LLMs in production, covering best practices for ensuring their performance and reliability. Finally, we will dive into the NVIDIA ecosystem and how it supports LLM deployment, enhancing model efficiency and scalability. By the end of the week, you will have a clear understanding of LLM deployment and monitoring techniques.