Introduction to Large Language Models

Nicolai Nielsen

47 Minutes
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"Introduction to Large Language Models" is an enlightening course designed to unveil the complexities and capabilities of today's most advanced AI technologies. Designed for enthusiasts, professionals, and anyone curious about the future of AI, this course demystifies large language models and provides you with the knowledge and skills to leverage their power in innovative ways. Whether you're looking to enhance your career or simply explore the capabilities of cutting-edge AI technologies, "Introduction to Large Language Models" offers a comprehensive guide to understanding and applying the principles of LLMs.

Explore the Course Plan
Module 1: Introduction to Large Language Models

This module introduces Large Language Models (LLMs), focusing on their fundamental concepts and applications.

Module 2: Dataset, Structure, and Applications of Large Language Models

This module delves into the intricacies of training large language models (LLMs) and transforming them into functional assistants.

Module 3: Real-World Applications and Production of Large Language Models

This module explores the practical application of large language models (LLMs) in real-world scenarios, focusing on how to implement them effectively outside of standard APIs like OpenAI.

Module 4: Large Language Model Security

This module explores the critical aspect of security in large language models (LLMs), discussing various methods used to exploit vulnerabilities in these systems, including jailbreaking, prompt injection, and data poisoning.

Module 5: Exploring ChatGPT's Advanced Features and Real-World Applications

In this module, we delve into the practical applications and advanced features of ChatGPT, showcasing its robust capabilities in generating and understanding text, images, and code.

Module 6: Building a PDF Document Q&A System with Large Language Models

This module demonstrates the development of a unique PDF document Q&A system using a retrieval augmented generation (RAG) approach.

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