Preface
Contents
About the Author
Chapter 1: A General Introduction to Artificial Intelligence
	1.1 The Concept of Artificial Intelligence
		1.1.1 What Is Artificial Intelligence?
		1.1.2 The Relationship Between AI, Machine Learning, and Deep Learning
		1.1.3 Types of AI
		1.1.4 The History of AI
		1.1.5 The Three Main Schools of AI
	1.2 AI-Related Technologies
		1.2.1 Deep Learning Framework
		1.2.2 An Overview of AI Processor
		1.2.3 Ecosystem of AI Industry
		1.2.4 Huawei CLOUD Enterprise Intelligence Application Platform
	1.3 The Technologies and Applications of AI
		1.3.1 The Technologies of AI
		1.3.2 The Applications of AI
		1.3.3 The Current Status of AI
	1.4 Huawei´s AI Development Strategy
		1.4.1 Full-Stack All-Scenario AI Solutions
		1.4.2 Directions of Huawei Full-Stack AI
	1.5 The Controversy of AI
		1.5.1 Algorithmic Bias
		1.5.2 Privacy Issues
		1.5.3 The Contradiction Between Technology and Ethics
		1.5.4 Will Everyone Be Unemployed?
	1.6 The Development Trends for AI
	1.7 Chapter Summary
	1.8 Exercises
Chapter 2: Machine Learning
	2.1 Introduction to Machine Learning
		2.1.1 Rational Understanding of Machine Learning Algorithms
		2.1.2 Major Problems Solved by Machine Learning
	2.2 Types of Machine Learning
		2.2.1 Supervised Learning
		2.2.2 Unsupervised Learning
		2.2.3 Semi-supervised Learning
		2.2.4 Reinforcement Learning
	2.3 The Overall Process of Machine Learning
		2.3.1 Data Collection
		2.3.2 Data Cleaning
		2.3.3 Feature Selection
		2.3.4 The Construction of Machine Learning Models
		2.3.5 Model Evaluation
	2.4 Model Parameters and Hyperparameters
		2.4.1 Gradient Descent
		2.4.2 Validation Set and Hyperparameter Search
		2.4.3 Cross-validation
	2.5 Common Algorithms of Machine Learning
		2.5.1 Linear Regression
		2.5.2 Logistic Regression
		2.5.3 Decision Tree
		2.5.4 Support Vector Machine
		2.5.5 K-Nearest Neighbor Algorithm
		2.5.6 Naive Bayes
		2.5.7 Ensemble Learning
		2.5.8 Clustering Algorithm
	2.6 Case Study
	2.7 Chapter Summary
	2.8 Exercises
Chapter 3: Overview of Deep Learning
	3.1 Introduction to Deep Learning
		3.1.1 Deep Neural Network
		3.1.2 The Development of Deep Learning
		3.1.3 Perceptron Algorithm
	3.2 Training Rules
		3.2.1 Loss Function
		3.2.2 Gradient Descent
		3.2.3 Backpropagation Algorithm
	3.3 Activation Function
	3.4 Regularization
		3.4.1 Parameter Norm Penalty
		3.4.2 Dataset Expansion
		3.4.3 Dropout
		3.4.4 Early Stopping
	3.5 Optimizer
		3.5.1 Momentum
		3.5.2 Adagrad Optimizer
		3.5.3 RMSprop Optimizer
		3.5.4 Adam Optimizer
	3.6 Types of Neural Network
		3.6.1 Convolutional Neural Network
		3.6.2 Recurrent Neural Network
		3.6.3 Generative Adversarial Network
	3.7 Common Problems
		3.7.1 Imbalanced Data
		3.7.2 Vanishing Gradient and Exploding Gradient
		3.7.3 Overfitting
	3.8 Chapter Summary
	3.9 Exercises
Chapter 4: Deep Learning Frameworks
	4.1 Introduction to Deep Learning Frameworks
		4.1.1 Introduction to PyTorch
		4.1.2 Introduction to MindSpore
		4.1.3 Introduction to TensorFlow
	4.2 TensorFlow 2.0 Basics
		4.2.1 Introduction to TensorFlow 2.0
		4.2.2 Introduction to Tensors
		4.2.3 TensorFlow 2.0 Eager Execution
		4.2.4 TensorFlow 2.0 AutoGraph
	4.3 Introduction to TensorFlow 2.0 Module
		4.3.1 Introduction to Common Modules
		4.3.2 Keras Interface
	4.4 Get Started with TensorFlow 2.0
		4.4.1 Environment Setup
		4.4.2 Development Process
	4.5 Chapter Summary
	4.6 Exercises
Chapter 5: Huawei MindSpore AI Development Framework
	5.1 Introduction to MindSpore Development Framework
		5.1.1 MindSpore Architecture
		5.1.2 How Is MindSpore Designed
		5.1.3 Advantages of MindSpore
	5.2 MindSpore Development and Application
		5.2.1 Environment Setup
		5.2.2 Components and Concepts Related to MindSpore
		5.2.3 Realization of Handwritten Digit Recognition with Mindspore
	5.3 Chapter Summary
	5.4 Exercises
Chapter 6: Huawei Atlas AI Computing Solution
	6.1 The Hardware Architecture of Ascend AI Processor
		6.1.1 The Logic Architecture of Ascend AI Processor Hardware
		6.1.2 Da Vinci Architecture
	6.2 The Software Architecture of Ascend AI Processor
		6.2.1 The Logic Architecture of Ascend AI Processor Software
		6.2.2 The Neural Network Software Flow of Ascend AI Processor
		6.2.3 Introduction to the Functional Modules of Ascend AI Processor Software Flow
		6.2.4 The Data Flow of Ascend AI Processor
	6.3 Atlas AI Computing Solution
		6.3.1 Atlas for AI Training Acceleration
		6.3.2 Atlas Device-Edge-Cloud Collaboration
	6.4 Industrial Implementation of Atlas
		6.4.1 Electricity: One-Stop ICT Smart Grid Solution
		6.4.2 Intelligent Finance: Holistic Digital Transformation
		6.4.3 Intelligent Manufacturing: Digital Integration of Machines and Thoughts
		6.4.4 Intelligent Transportation: Easier Travel and Improved Logistics
		6.4.5 Super Computer: Building State-Level AI Platform
	6.5 Chapter Summary
	6.6 Exercises
Chapter 7: HUAWEI AI Open Platform
	7.1 Introduction to HUAWEI HiAI Platform
		7.1.1 Architecture of HUAWEI HiAI Platform
		7.1.2 HUAWEI HiAI Foundation
		7.1.3 HUAWEI HiAI Engine
		7.1.4 HUAWEI HiAI Service
	7.2 Application Development Based on HUAWEI HiAI Platform
	7.3 Part of the HUAWEI HiAI Solutions
		7.3.1 HUAWEI HiAI Helping the Deaf and Dumb
		7.3.2 HUAWEI HiAI Enhancing the Visual Effects of Yuanbei Driving Test Application
		7.3.3 HUAWEI HiAI Empowering Ctrip Travel
		7.3.4 HUAWEI HiAI Empowering WPS Document Error Detection and Correction
	7.4 Chapter Summary
	7.5 Exercises
Chapter 8: Huawei CLOUD Enterprise Intelligence Application Platform
	8.1 Huawei CLOUD EI Service Family
		8.1.1 Huawei CLOUD EI Agent
		8.1.2 EI Basic Platform: Huawei HiLens
		8.1.3 EI Basic Platform: Graph Engine Service
		8.1.4 Introduction to Other Services Provided by EI Family
	8.2 ModelArts
		8.2.1 Functions of ModelArts
		8.2.2 Product Structure and Application of ModelArts
		8.2.3 Product Advantages of ModelArts
		8.2.4 Approaches of Visiting ModelArts
		8.2.5 How to Use ModelArts
	8.3 Huawei CLOUD EI Solutions
		8.3.1 OCR Service Enabling Whole-Process Automated Reimbursement
		8.3.2 OCR Supporting Smart Logistics
		8.3.3 Conversational Bot
		8.3.4 A Case of Enterprise Intelligent Q&A in a Certain District
		8.3.5 A Case in Genetic Knowledge Graph
		8.3.6 Policy Query Based on Knowledge Graph
		8.3.7 A Case in Smart Park
		8.3.8 A Case in Pedestrian Counting and Heat Map
		8.3.9 A Case in Vehicle Recognition
		8.3.10 A Case in Intrusion Identification
		8.3.11 CNPC Cognitive Computing Platform: Reservoir Identification for Well Logging
	8.4 Chapter Summary
	8.5 Exercises
	Appendix 1: Introduction to the API of HiAI Engine
		Face Recognition
		Human Recognition
		Image Recognition
		Code Recognition
		Video Technology
		Text Recognition
		Speech Recognition
		Natural Language Processing
	Appendix 2: Key to Exercises
Index