Large Language Models (LLMs) & Text Generation

Overview

Dive deeper into how computers understand and create language, and learn how to build a custom chatbot using unsupervised machine learning, prompt engineering, and retrieval augmented generation. We'll start with a high-level overview of the types of LLMs, the differences between them, and how best to account for their strengths and weaknesses. Then we'll get into the internal details, including natural language processing (NLP) techniques like tokenization, as well as modern transformer architectures and attention mechanisms. Finally, we'll build a practical LLM application that combines an LLM with a custom dataset.

Syllabus

Introduction to LLMs

This lesson covers the types of LLMs, an intuitive understanding of their limitations and capabilities, inference and decoding hyperparameters, and strategies for effective prompt engineering.

NLP Fundamentals

This lesson covers the essential Natural Language Processing topics needed to use the latest LLM technology. You will learn the basics of NLP and then dive into text encoding and text generation.

Transformers and Attention Mechanism

In this lesson, you will open up the black box of transformer architectures and learn about the attention mechanisms and other components that make these powerful models possible.

Retrieval Augmented Generation

In this lesson, we will learn how to create a custom Q&A bot powered by OpenAI! Along the way, you'll learn how OpenAI works and how to leverage its powerful language processing capabilities.

Build Custom Datasets for LLMs

In this lesson, you will learn how to construct a relevant, quality dataset for fine-tuning large language models and performing retrieval augmented generation.