Book Image

AI Crash Course

By : Hadelin de Ponteves
5 (2)
Book Image

AI Crash Course

5 (2)
By: Hadelin de Ponteves

Overview of this book

Welcome to the Robot World … and start building intelligent software now! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of thousands of people to write AI software. Now, for the first time, his hands-on, energetic approach is available as a book. Starting with the basics before easing you into more complicated formulas and notation, AI Crash Course gives you everything you need to build AI systems with reinforcement learning and deep learning. Five full working projects put the ideas into action, showing step-by-step how to build intelligent software using the best and easiest tools for AI programming, including Python, TensorFlow, Keras, and PyTorch. AI Crash Course teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination.
Table of Contents (17 chapters)
16
Index

The five principles of Reinforcement Learning

Let's begin building the first pillars of your intuition into how Reinforcement Learning works. These are the fundamental principles of Reinforcement Learning, which will get you started with the right, solid basics in AI.

Here are the five principles:

  1. Principle #1: The input and output system
  2. Principle #2: The reward
  3. Principle #3: The AI environment
  4. Principle #4: The Markov decision process
  5. Principle #5: Training and inference

In the following sections, you can read about each one in turn.

Principle #1 – The input and output system

The first step is to understand that today, all AI models are based on the common principle of inputs and outputs. Every single form of Artificial Intelligence, including Machine Learning models, ChatBots, recommender systems, robots, and of course Reinforcement Learning models, will take something as input, and will return another thing as output.

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