醉染图书AI速成课程()(英文版)9787564189709

Preface
Chapter 1:Welcome to the Robot World
Beginning the AI journey
Four different AI models
The models in practice
Fundamentals
Thompson Sampling
-learning
Deep -learning
Deep convolutional -learning
Where can learning AI take you?
Energy
Healthcare
Transporndlgistics
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 oraios
Exerc=se
Lists and arrays
Exercise
if statemensndcnditions
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 inpundutput 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
Lesstrtcding
The final result
Summary
Chapter 7: Welcome to -Learning
The Maze
Beginnings
Building the environment
The states
The actions
The rewards
Building the AI
The -value
The temporal difference
The Bellman equation
Reinforcement intuition
The whole -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 -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 -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 -learning
The Softmax method
Deep -learning recap
Experience replay
The whole deep -learning algorithm
Summary
Chapter 10: AI for Autonomous Vehicles -Build a Self-Driving Car
Building the environment
Defining the goal
Setting

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.