Four different AI models
These AI models were chosen to be part of this book because they are used in a great variety of industry applications and can solve many different real-world problems. I'll just reveal their names here before we study them in depth across the book. The four AI models you will learn everything about in this book are the following:
- Thompson Sampling
- Q-learning
- Deep Q-learning
- Deep convolutional Q-learning
For each of these four models, we will follow the same three-step approach:
- Get an intuitive understanding of how it works.
- Get all the math behind the theory.
- Implement the model from scratch in Python.
I have followed this structure many times with my students, and I can tell you that it works the best. The idea is simple: because you start with your intuition, you won't get overwhelmed by the math, but will instead understand it more easily. You'll also feel comfortable coding some models of which you both have an intuitive understanding and in-depth theoretical knowledge.
The models in practice
All the way through this book you'll find practical examples to learn from or implement yourself. Here's a list of the AI implementations you'll find in the chapters of this course, which start in Chapter 3 after you get the tools you need for your AI journey in Chapter 2.
Fundamentals
Chapter 3, Python Fundamentals – Learn How to Code in Python, contains the Python coding fundamentals you'll need for this book. You can remind yourself, or learn from scratch, how to code in Python.
Chapter 4, AI Foundation Techniques, contains a pseudocode example to illustrate the five core principles of Artificial Intelligence.
Thompson Sampling
Chapter 5, Your First AI Model – Beware the Bandits!, contains introductory code to illustrate the theory behind the Thompson Sampling AI model.
Chapter 6, AI for Sales and Advertising – Sell like the Wolf of AI Street, contains a real-world implementation of the Thompson Sampling model, applied to online advertising.
Q-learning
Chapter 7, Welcome to Q-Learning, contains pseudocode to illustrate the theory of the Q-learning AI model.
Chapter 8, AI for Logistics – Robots in a Warehouse, contains a real-world implementation of the Q-learning model, applied to process automation and optimization.
Deep Q-learning
Chapter 9, Going Pro with Artificial Brains – Deep Q-Learning, contains introductory code to illustrate the theory behind Artificial Neural Networks.
Chapter 10, AI for Autonomous Vehicles – Build a Self-Driving Car, contains a real-world implementation of the Deep Q-learning model, applied to self-driving cars.
Chapter 11, AI for Business, contains another real-world implementation of the Deep Q-learning model, applied to energy and business.
Deep convolutional Q-learning
Chapter 12, Deep Convolution Q-Learning, contains introductory code to illustrate the implementation of a Convolutional Neural Network (CNN).
Chapter 13, AI for Video Games – Become the Master at Snake, contains a real-world implementation of the deep convolutional Q-learning model applied to a game.
As you can see, every time you're introduced to a new model, you learn the intuition first, then the math, and then you move to an implementation of the model. So, why is learning how to implement these models worth your while?
