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

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.