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AI Crash Course

You're reading from   AI Crash Course A fun and hands-on introduction to machine learning, reinforcement learning, deep learning, and artificial intelligence with Python

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Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781838645359
Length 360 pages
Edition 1st Edition
Languages
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Author (1):
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Hadelin de Ponteves Hadelin de Ponteves
Author Profile Icon Hadelin de Ponteves
Hadelin de Ponteves
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Toc

Table of Contents (17) Chapters Close

Preface 1. Welcome to the Robot World 2. Discover Your AI Toolkit FREE CHAPTER 3. Python Fundamentals – Learn How to Code in Python 4. AI Foundation Techniques 5. Your First AI Model – Beware the Bandits! 6. AI for Sales and Advertising – Sell like the Wolf of AI Street 7. Welcome to Q-Learning 8. AI for Logistics – Robots in a Warehouse 9. Going Pro with Artificial Brains – Deep Q-Learning 10. AI for Autonomous Vehicles – Build a Self-Driving Car 11. AI for Business – Minimize Costs with Deep Q-Learning 12. Deep Convolutional Q-Learning 13. AI for Games – Become the Master at Snake 14. Recap and Conclusion 15. Other Books You May Enjoy 16. Index

Recap – The general AI framework/Blueprint

Let's recap the whole AI Blueprint, so that you can print it out and put it on your wall.

Step 1: Building the environment

  • Step 1-1: Introducing and initializing all the parameters and variables of the environment.
  • Step 1-2: Making a method that updates the environment right after the AI plays an action.
  • Step 1-3: Making a method that resets the environment.
  • Step 1-4: Making a method that gives us at any time the current state, the last reward obtained, and whether the game is over.

Step 2: Building the brain

  • Step 2-1: Building the input layer composed of the input states.
  • Step 2-2: Building the hidden layers with a chosen number of these layers and neurons inside each, fully connected to the input layer and between each other.
  • Step 2-3: Building the output layer, fully connected to the last hidden layer.
  • Step 2-4: Assembling the full architecture inside a model...
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