Introduction to Large Language Models
Chapter 1: Understanding Large Language Models
What is Artificial Intelligence? A Brief Overview
Introducing Natural Language Processing (NLP)
Defining Large Language Models (LLMs)
How LLMs Learn from Text Data
Examples of Tasks LLMs Can Perform
Common Misconceptions about LLMs
Quiz for Chapter 1
Chapter 2: The Mechanics of LLMs (Simplified)
Representing Words: Tokens and Embeddings
Predicting the Next Word: The Core Idea
The Role of Training Data Size
Understanding Model Parameters
Introduction to Transformer Architecture (High-Level)
How Context Influences Generation
Quiz for Chapter 2
Chapter 3: Communicating with LLMs: Prompts
What is a Prompt?
Basic Prompting Techniques
Providing Instructions Clearly
Giving Examples (Few-Shot Prompting)
Controlling Output Length and Format
Common Prompting Challenges
Practice: Crafting Your First Prompts
Quiz for Chapter 3
Chapter 4: A Look at Different LLMs
Overview of Foundational Models
General Purpose vs. Specialized Models
Open vs. Closed Models: What's the Difference?
Understanding Model Size and Capabilities
Accessing Models: APIs and Interfaces
Quiz for Chapter 4
Chapter 5: Using Pre-trained LLMs
What are Pre-trained Models?
Finding and Choosing an LLM Service
Interacting via Web Interfaces
Introduction to Using LLM APIs
Sending Your First API Request
Interpreting LLM Responses
Hands-on Practical: Simple Text Generation Task
Quiz for Chapter 5
Understanding Model Parameters
Was this section helpful?
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A foundational textbook providing an academic introduction to deep learning, covering model parameters, learning algorithms, and neural network architectures. Essential for understanding the theoretical basis of LLM parameters.
- Attention Is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin, 2017 Advances in Neural Information Processing Systems 30 (NIPS 2017) (Curran Associates, Inc.) - The seminal paper introducing the Transformer architecture, which forms the basis of modern LLMs. It details the model structure where billions of parameters reside and how they enable language processing.
- CS224N: Natural Language Processing with Deep Learning, Diyi Yang, Tatsunori Hashimoto, 2025 (Stanford University) - A comprehensive university course covering deep learning fundamentals applied to NLP, including detailed explanations of model parameters, neural network architectures, and the Transformer model. Provides accessible educational context.