5 - Part 1 : NLP - Introduction

5 - Part 1 : NLP - Introduction

In this introduction we talk about representing words.

I also introduce binary notation -- don't be scared! and it's not essential you remember or understand it -- but it is a very useful way to bring home just how different a word is to us, where it carries real meaning and a word as it is represented in a computer.

Illustrating this point really brings home just how much work AI algorithms have to do in order to process, use, and generate the type of language we deal with everyday and take for granted.

I'd definitely recommend a screen for this and all the videos in this chapter.

Introduction To AI

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1: Introduction

  • Chapter 1 - Course Introduction
  • 1 - Course Introduction

2. What is AI?

  • Chapter 2 - What Is AI?
  • 2 - Part 1 : What is AI?
  • 2 - Part 2 : How To Test For AI
  • 2 - Part 3 : Ok, So What is AI?

3. Why Now?

  • Chapter 3 - Why Now?
  • 3 - Part 1 : Why Now? - Introduction
  • 3 - Part 2 : Advances In AI
  • 3 - Part 3 : Further Reasons

4. AI for playing Games

  • Chapter 4 - AI for Playing Games
  • 4 - Part 1 : AI for Games - Introduction
  • 4 - Part 2 : Introducing Game Trees
  • 4 - Part 3 : Playing an Opponent
  • 4 - Part 4 : Getting smarter with search
  • 4 - Part 5 : Self-Play and MonteCarlo Search

5. Natural Language Processing

  • Chapter 5 - Natural Language Processing (NLP)
  • 5 - Part 1 : NLP - Introduction
  • 5 - Part 2 : Representing Words and your first Machine Learning algorithm
  • 5 - Part 3 : What are Embeddings?
  • 5 - Part 4 : Generating Text

6. Neural Networks

  • Chapter 6 - Neural Networks
  • 6 - Part 1 : Introduction and Perceptrons
  • 6 - Part 2 : Training a Perceptron
  • 6 - Part 3 : Linking Neurons - Building Larger Networks
  • 6 - Part 4: Long Short-Term Memory Networks

7. Reinforcement Learning

  • Chapter 7 - Reinforcement Learning
  • 7 - Part 1 : Reinforcement Learning - Introduction
  • 7 - Part 2 : Q-Learning - Escaping Mazes!
  • 7 - Part 3 : Beyond Mazes

8. Large Language Models and ChatGPT

  • Chapter 8 - Large Language Models and ChatGPT
  • 8 - Part 1 : Introduction and Transformer Model Encoders
  • 8 - Part 2 : Decoders and Large Language Models

9. Getting more out of Large Language Models and AI applications

  • Chapter 9 - Getting more out of LLMs: Grounding, Tools, Agents, and Prompting strategies
  • 9 - Part 1 : Getting more out of LLMs
  • 9 - Part 2 : Demystifying LLM tooling, RAG, MCP, and Agents

10. Computer Vision and Image Generation

  • Chapter 10 - Computer Vision and Image Generation
  • 10 - Part 1 : How AI understands images
  • 10 - Part 2 : Learning Image Features
  • 10 - Part 3 : How to generate images and videos

11. A World with AI - AI in Society and Business

  • Chapter 11 - A world with AI: AI in Society and Business
  • 11 - Part 1 : A world with AI - Introduction
  • 11- Part 2 : AI in business in context
  • 11 - Part 3 : Role Evolution and Business Adoption

12. AI Risks and Regulation

  • Chapter 12 - Risks and Regulation.pdf
  • 12 - Part 1 : Framing the risks
  • 12- Part 2 : Red Teaming, Regulation and the AI opportunity

13. Course Summary

  • Course Summary
  • Course Summary