From the course: Building Secure and Trustworthy LLMs Using NVIDIA Guardrails
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What you should know
From the course: Building Secure and Trustworthy LLMs Using NVIDIA Guardrails
What you should know
- [Narrator] Before we dive into the world of NVIDIA Guardrails for LLMs, let's ensure you have the right foundation to get the most out of the course. Understanding Python is essential and required for this course. We'll be diving into practical coding exercises that involve data manipulation and implementing some of these NVIDIA Guardrails. It would be great if you're also familiar with Jupyter Notebooks, which we'll be using extensively for some of the interactive coding sessions. And while not mandatory basing understanding of machine learning concepts such as neural networks, lost landscapes, and gradient descent will be incredibly beneficial. Additionally, having some knowledge of large language models, their functions, structure and applications will help you grasp the course content more effectively. Don't worry if you're not an expert in these areas, a basic understanding will suffice. This course is designed to guide you through the complexities of implementing guardrails in…
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