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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 FREE CHAPTER 2. Discover Your AI Toolkit 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

What this book covers

Chapter 1, Welcome to the Robot World, introduces you to the world of Artificial Intelligence.

Chapter 2, Discover Your AI Toolkit, uncovers an easy-to-use toolkit of all the AI models as Python files, ready to run thanks to the amazing Google Colaboratory platform.

Chapter 3, Python Fundamentals – Learn How to Code in Python, provides the right Python fundamentals and teaches you how to code in Python.

Chapter 4, AI Foundation Techniques, introduces you to reinforcement learning and its five fundamental principles.

Chapter 5, Your First AI Model – Beware the Bandits!, teaches the theory of the multi-armed bandit problem and how to solve it in the best way with the Thompson Sampling AI model.

Chapter 6, AI for Sales and Advertising – Sell like the Wolf of AI Street, applies the Thompson Sampling AI model of Chapter 5 to solve a real-world business problem related to sales and advertising.

Chapter 7, Welcome to Q-Learning, introduces the theory of the Q-learning AI model.

Chapter 8, AI for Logistics – Robots in a Warehouse, applies the Q-learning AI model of Chapter 7 to solve a real-world business problem related to logistics optimization.

Chapter 9, Going Pro with Artificial Brains – Deep Q-Learning, introduces the fundamentals of deep learning and the theory of the deep Q-learning AI model.

Chapter 10, AI for Autonomous Vehicles – Build a Self-Driving Car, applies the deep Q-learning AI model of Chapter 9 to build a virtual self-driving car.

Chapter 11, AI for Business – Minimize Cost with Deep Q-Learning, applies the deep Q-learning AI model of Chapter 9 to solve a real-world business problem related to cost optimization.

Chapter 12, Deep Convolutional Q-Learning, introduces the fundamentals of convolutional neural networks and the theory of the deep convolutional Q-learning AI model.

Chapter 13, AI for Games – Become the Master at Snake, applies the deep convolutional Q-learning AI model of Chapter 12 to beat the famous Snake video game

Chapter 14, Recap and Conclusion, concludes the book with a recap of how to create an AI framework and some final words from the author about your future in the world of AI.

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