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  • Book Overview & Buying AI Crash Course
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AI Crash Course

AI Crash Course

By : Hadelin de Ponteves
4 (23)
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AI Crash Course

AI Crash Course

4 (23)
By: Hadelin de Ponteves
Buy this Book

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)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Get in touch
Lock Free Chapter
1
Welcome to the Robot World
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Welcome to the Robot World
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Beginning the AI journey
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Four different AI models
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Where can learning AI take you?
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Summary
2
Discover Your AI Toolkit
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Discover Your AI Toolkit
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The GitHub page
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Colaboratory
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Summary
3
Python Fundamentals – Learn How to Code in Python
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Python Fundamentals – Learn How to Code in Python
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Displaying text
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Variables and operations
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Lists and arrays
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if statements and conditions
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for and while loops
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Functions
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Classes and objects
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Summary
4
AI Foundation Techniques
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AI Foundation Techniques
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What is Reinforcement Learning?
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The five principles of Reinforcement Learning
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Summary
5
Your First AI Model – Beware the Bandits!
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Your First AI Model – Beware the Bandits!
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The multi-armed bandit problem
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The Thompson Sampling model
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Summary
6
AI for Sales and Advertising – Sell like the Wolf of AI Street
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AI for Sales and Advertising – Sell like the Wolf of AI Street
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Problem to solve
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Building the environment inside a simulation
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AI solution and intuition refresher
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Implementation
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Summary
7
Welcome to Q-Learning
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Welcome to Q-Learning
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The Maze
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The whole Q-learning process
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Summary
8
AI for Logistics – Robots in a Warehouse
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AI for Logistics – Robots in a Warehouse
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Building the environment
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Implementation
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Summary
9
Going Pro with Artificial Brains – Deep Q-Learning
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Going Pro with Artificial Brains – Deep Q-Learning
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Predicting house prices
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Deep learning theory
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Deep Q-learning
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Summary
10
AI for Autonomous Vehicles – Build a Self-Driving Car
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AI for Autonomous Vehicles – Build a Self-Driving Car
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Building the environment
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AI solution refresher
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Implementation
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The demo
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Summary
11
AI for Business – Minimize Costs with Deep Q-Learning
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AI for Business – Minimize Costs with Deep Q-Learning
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Problem to solve
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Building the environment
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AI solution
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The demo
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Recap – The general AI framework/Blueprint
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Summary
12
Deep Convolutional Q-Learning
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Deep Convolutional Q-Learning
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What are CNNs used for?
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How do CNNs work?
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Deep convolutional Q-learning
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Summary
13
AI for Games – Become the Master at Snake
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AI for Games – Become the Master at Snake
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Problem to solve
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Building the environment
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AI solution
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Implementation
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The demo
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Summary
14
Recap and Conclusion
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Recap and Conclusion
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Recap – The general AI framework/blueprint
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Exploring what's next for you in AI
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Other Books You May Enjoy
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Other Books You May Enjoy
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Leave a review - let other readers know what you think
16
Index
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Index

Summary

In this chapter, we learned how to build a deep Q-learning model to drive a self-driving car. As inputs it took the information from the three sensors and its current orientation. As outputs it decided the Q-values for each of the actions of going straight, turning left, or turning right. As for the rewards, we punished it badly for hitting the sand, punished it slightly for going in the wrong direction, and rewarded it slightly for going in the right direction. We made the AI implementation in PyTorch and used Kivy for the graphics. To run all of this we used the Anaconda environment.

Now take a long break, you deserve it! I'll see you in the next chapter for our next AI challenge, where this time we will solve a real-world business problem with cost implications running into the millions.

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83
Tech Concepts
36
Programming languages
73
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AI Crash Course
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