Module 1: Introduction to Large Language Models
Module Overview
This introductory module provides a comprehensive foundation for understanding Large Language Models (LLMs). We'll explore what makes these models "large," trace their evolution from earlier approaches, and survey the current landscape of available models.
Learning Objectives
By the end of this module, you will be able to:
- ✅ Define what constitutes a Large Language Model
- ✅ Explain the key characteristics that distinguish LLMs from earlier models
- ✅ Describe the historical evolution from RNNs to Transformers
- ✅ Identify major LLM providers and their offerings
- ✅ Compare open-source versus proprietary models
Module Contents
Chapter 1: What are Large Language Models?
Explore the definition, scale characteristics, and emergent capabilities of LLMs. Understand what makes them different from smaller language models.
Chapter 2: History - From RNNs to Transformers
Trace the evolution of neural language models from Recurrent Neural Networks through LSTMs to the revolutionary Transformer architecture.
Chapter 3: The LLM Landscape Today
Survey the current ecosystem of LLMs, including major players like OpenAI, Anthropic, Google, and Meta, as well as the vibrant open-source community.
Key Concepts Preview
| Concept | Definition |
|---|---|
| Large Language Model | Neural network with billions of parameters trained on vast text corpora |
| Emergent Capabilities | Abilities that arise only at scale, not present in smaller models |
| Transformer | Architecture based on self-attention, enabling parallel processing |
| Pre-training | Initial training on large unlabeled text data |
| Scaling Laws | Predictable relationship between model size, data, and performance |
Recommended Reading
Before diving into this module, consider reviewing:
- Attention Is All You Need - The original Transformer paper
- Language Models are Few-Shot Learners - GPT-3 paper
Time Estimate
- Reading: 2-3 hours
- Quiz: 30-45 minutes
- Total: 3-4 hours