AI crash course
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作者(法)哈德琳·德.庞特维斯|责编:张烨
出版社东南大学出版社
ISBN9787564189709
出版时间2020-08
装帧平装
开本16开
定价108元
货号9787564189709
上书时间2024-11-15
商品详情
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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 Artifi 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, artifi 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
Lets 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 Artifi 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
Artifi 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
内容摘要
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通过他很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抢先发售以专著形式出版了。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很简单的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。
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