AI Crash Course: A Fun and Hands-On Introduction to Reinforcement Learning, Deep Learning, and Artificial Intelligence with Python

Description

This friendly and accessible guide to AI theory and programming in Python requires no maths or data science background. Key Features Roll up your sleeves and start programming AI models No math, data science, or machine learning background required Packed with hands-on examples, illustrations, and clear step-by-step instructions 5 hands-on working projects put ideas into action and show step-by-step how to build intelligent software Book Description AI is changing the world - and with this book, anyone can start building intelligent software! 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. Taking a graduated approach that starts with the basics before easing readers into more complicated formulas and notation, Hadelin helps you understand what you really 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: Google Colab Python TensorFlow Keras 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. What you will learn Master the key skills of deep learning, reinforcement learning, and deep reinforcement learning Understand Q-learning and deep Q-learning Learn from friendly, plain English explanations and practical activities Build fun projects, including a virtual-self-driving car Use AI to solve real-world business problems and win classic video games Build an intelligent, virtual robot warehouse worker Who this book is for If you want to add AI to your skillset, this book is for you. It doesn't require data science or machine learning knowledge. Just maths basics (high school level). Cover Copyright Packt Page Contributors Table of Contents Preface Chapter 1: Welcome to the Robot World Beginning the AI journey Four different AI models The models in practice Fundamentals Thompson Sampling Q-learning Deep Q-learning Deep convolutional Q-learning Where can learning AI take you? Energy Healthcare Transport and logistics Education Security Employment Smart homes and robots Entertainment and happiness Environment Economy, business, and finance Summary Chapter 2: Discover Your AI Toolkit The GitHub page Colaboratory Summary Chapter 3: Python Fundamentals – Learn How to Code in Python Displaying text Exercise Variables and operations Exercise Lists and arrays Exercise if statements and conditions Exercise for and while loops Exercise Functions Exercise Classes and objects Exercise Summary Chapter 4: AI Foundation Techniques What is Reinforcement Learning? The five principles of Reinforcement Learning Principle #1 – The input and output system Principle #2 – The reward Principle #3 – The AI environment Principle #4 – The Markov decision process Principle #5 – Training and inference Training mode Inference mode Summary Chapter 5: Your First AI Model – Beware the Bandits! The multi-armed bandit problem The Thompson Sampling model Coding the model Understanding the model What is a distribution? Tackling the MABP The Thompson Sampling strategy in three steps The final touch of shaping your Thompson Sampling intuition Thompson Sampling against the standard model Summary Chapter 6: AI for Sales and Advertising – Sell like the Wolf of AI Street Problem to solve Building the environment inside a simulation Running the simulation Recap AI solution and intuition refresher AI solution Intuition Implementation Thompson Sampling vs. Random Selection Performance measure Let's start coding The final result Summary Chapter 7: Welcome to Q-Learning The Maze Beginnings Building the environment The states The actions The rewards Building the AI The Q-value The temporal difference The Bellman equation Reinforcement intuition The whole Q-learning process Training mode Inference mode Summary Chapter 8: AI for Logistics – Robots in a Warehouse Building the environment The states The actions The rewards AI solution refresher Initialization (first iteration) Next iterations Implementation Part 1 – Building the environment Part 2 – Building the AI Solution with Q-learning Part 3 – Going into production Improvement 1 – Automating reward attribution Improvement 2 – Adding an intermediate goal Summary Chapter 9: Going Pro with Artificial Brains – Deep Q-Learning Predicting house prices Uploading the dataset Importing libraries Excluding variables Data preparation Scaling data Building the neural network Training the neural network Displaying results Deep learning theory The neuron Biological neurons Artificial neurons The activation function The threshold activation function The sigmoid activation function The rectifier activation function How do neural networks work? How do neural networks learn? Forward-propagation and back-propagation Gradient Descent Batch gradient descent Stochastic gradient descent Mini-batch gradient descent Deep Q-learning The Softmax method Deep Q-learning recap Experience replay The whole deep Q-learning algorithm Summary Chapter 10: AI for Autonomous Vehicles – Build a Self-Driving Car Building the environment Defining the goal Setting the parameters The input states The output actions The rewards AI solution refresher Implementation Step 1 – Importing the libraries Step 2 – Creating the architecture of the neural network Step 3 – Implementing experience replay Step 4 – Implementing deep Q-learning The demo Installing Anaconda Creating a virtual environment with Python 3.6 Installing PyTorch Installing Kivy Summary Chapter 11: AI for Business – Minimize Costs with Deep Q-Learning Problem to solve Building the environment Parameters and variables of the server environment Assumptions of the server environment Assumption 1 – We can approximate the server temperature Assumption 2 – We can approximate the energy costs Simulation Overall functioning Defining the states Defining the actions Defining the rewards Final simulation example AI solution The brain Implementation Step 1 – Building the environment Step 2 – Building the brain Without dropout With dropout Step 3 – Implementing the deep reinforcement learning algorithm Step 4: Training the AI No early stopping Early stopping Step 5 – Testing the AI The demo Recap – The general AI framework/Blueprint Summary Chapter 12: Deep Convolutional Q-Learning What are CNNs used for? How do CNNs work? Step 1 – Convolution Step 2 – Max pooling Step 3 – Flattening Step 4 – Full connection Deep convolutional Q-learning Summary Chapter 13: AI for Games – Become the Master at Snake Problem to solve Building the environment Defining the states Defining the actions Defining the rewards AI solution The brain The experience replay memory Implementation Step 1 – Building the environment Step 2 – Building the brain Step 3 – Building the experience replay memory Step 4 – Training the AI Step 5 – Testing the AI The demo Installation The results Summary Chapter 14: Recap and Conclusion Recap – The general AI framework/blueprint Exploring what's next for you in AI Practice, practice, and practice Networking Never stop learning Other Books You May Enjoy Index