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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 (24)
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AI Crash Course

AI Crash Course

4 (24)
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
15
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

AI solution

As always, the AI solution for deep Q-learning consists of two parts:

  1. Brain – the neural network that will learn and take actions
  2. Experience replay memory – the memory that will store our experience; the neural network will learn from this memory

Let's tackle those now!

The brain

This part of the AI solution will be responsible for teaching, storing, and evaluating our neural network. To build it, we're going to use a CNN!

Why a CNN? When explaining the theory behind them, I mentioned that they're often used when "our environment as state returns images," and that's exactly what we're dealing with here. We've already established that the game state is going to be a stacked 3D array containing the last few game frames.

In the previous chapter, we discussed that a CNN takes a 2D image as input, not a stacked 3D array of images; but do you remember this graphic?

https://lh5.googleusercontent.com/qjfDY_d7Dvn92gkZ2KDpPAoy-SM_7AO8RExLTjtj-FYCQcCDVIrfSjvgslPBBT5kAneqJMRbJKAOikeslS-1T5TQaPDDxX338ko4DWQxi5xPggLbosb-p3tR8y5DDGp-blxs1aqj

Figure 4...

CONTINUE READING
83
Tech Concepts
36
Programming languages
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AI Crash Course
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