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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

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

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

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

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

Introduction to Large Language Models

Prerequisites: No specific prerequisites

Level: Beginner

What You'll Learn

LLM Fundamentals
Define Large Language Models and explain their relationship to AI and NLP.
Core Mechanics
Describe how LLMs process text and generate responses at a high level.
Prompting Basics
Understand the concept of prompts and how to craft simple instructions for LLMs.
Model Interaction
Interact with pre-trained LLMs using web interfaces or basic API calls.
Model Types
Recognize different types of LLMs and their general characteristics.

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