AI正在改变世界——有了这本书，任何人都可以动手构建智能软件！
Hadel in de Pontaves通过他最畅销的视频课程教会了成千上万的人编写AI软件。如今，其充满活力的动手实践方法首次以专著形式出版了。Hadel in采取渐进式的方法，先从基础知识入手，再将读者引向更复杂的公式和记法，帮助你理解使用强化学习和深度学习构建AI系统真正需要的是什么。通过5个完整的工作项目将这些理念付诸实践，一步步展示了如何使用很好和最简单的AI编程工具来构建智能软件：
·Google Colab
·Python
·TensorFlow
·Keras
·PyTorch
本书将教会大家如何构建一个能够在应用中发挥作用的AI。读完这本书后，能你的就只有想象力了。
作者简介
哈德琳·德.庞特维斯，Hadelin de Ponteves is the co-founder and CEO at BlueLife AI, which leverages the power of cutting-edge Artificial Intelligence to empower businesses to make massive profits by optimizing processes, maximizing efficiency, and increasing profitability. Hadelin is also an online entrepreneur who has created 50+ top-rated educational e-courses on topics such as machine learning, deep learning, artificial intelligence, and blockchain, which have reached over 700,000 subscribers in 204 countries.
目录
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 Exerc=se 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
