Target Audience
• Participants who hope to become AI engineers.
• Participants who hope to obtain an HCIA.
• Participants who want to acquire AI certificate.
• Those who hope to know how to use, manage, and maintain Huawei AI products and AI cloud services.
Overview:
This program provides a deep understanding of AI. It provides an outlook over AI evolution between past, present, and future. It also provides an understanding of the development of AI industries, strategic planning of AI in the world, justice, and equity in the era of AI, man machine Relationship in the era of AI, and AI governance.
The program also tackles Phyton programing basics, basic math, and an introduction to TensorFlow. It also goes into deep learning frameworks including a propaedeutic of deep learning and an overview of deep learning.
The program is experimental and interactive and consists of lectures, demonstrations, and experiments to optimize learning experience.
Course Objectives
After completing these courses, participants will be able to:
1. Understand the overview of AI.
2. Master the Python programming language.
3. Master the Math basics required for deep learning.
4. Understand the overview of the TensorFlow.
5. Understand the propaedeutic and overview of deep learning.
6. Understand the overview of Huawei cloud EI.
7. Know how to perform basic programming using Python.
8. Know how to perform mathematical programming using Python.
9. Know how to perform basic programming using TensorFlow.
10.Know how to perform basic programming for image recognition.
11.Know how to perform basic programming for speech recognition.
12.Know how to perform basic programming for man machine dialogs.
Course Outline
Day One:
Overview of AI
• The Past, Present, and Future of AI
• Development of AI Industries
• Strategic Planning of AI in the World
• Justice and Equity in the Era of AI
• Man Machine Relationship in the Era of AI
• AI Governance
• AI Society in the Future
Phyton Programing
• Introduction to Python
• List and Tuple
• String
• Dictionary
• Conditional and Loop Statements
• Function
• Object Oriented Programming
• Date and Time
• Regular Expression
• File Manipulation
Day Two:
Basic Math
• Linear Algebra
- Special Matrices
- Eigendecomposition
- Singular Value Decomposition
- Moore Penrose Pseudoinverse
- Trace Operator
- Determinants
- Example: Principal Component Analysis
• Probability and Information Theory
- Random Variables
- Probability Distribution
- Marginal Probability
- Conditional Probability
- Independence and Condition al Independence
- Expectation, Variance, and Covariance
- Common Probability Distribution
- Bayesian Rules
- Continuous Variable
- Information Theory
- Structured Statistical Model
• Numeric Calculation
- Overflow and Underflow
- Ill Condition
- Gradient Based Optimization Method
- Constraint Optimization
- Example: Linear Least squares
Introduction to TensorFlow
• What Is TensorFlow?
• TensorFlow Characteristics
• TensorFlow Basics
• TensorFlow Modules
• Development Environment Deployment
• Basic Development Steps Using TensorFlow
- Defining the TensorFlow Input Node
- Defining the Learning Parameter Variable
- Defining the Operation
- Optimizing Functions and Objectives
- Initializing All Variables
- Iterate and Update Parameters to the Optimal Solution
- Testing the Model
- Using the Model
• Other Deep Learning Frameworks
Day Three:
Propaedeutics and Overview of Deep Learning
• Propaedeutics of Deep Learning
- Learning Algorithms
- Common Machine Learning Algorithms
- Hyperparameter and Validation Set
- Parameter Estimation
- Maximum Likelihood Estimation
- Bayes Estimation
• Overview of Deep Learning
- Definition and Development of Neural Networks
- Perceptron and Training Rules
- Activation Functions
- Types of Neural Networks
- Regularization in Deep Learning
- Optimizer
- Applications of Deep Learning
Huawei Cloud EI Overview
• Concept of AI and Origin of EI
• Details About Huawei Cloud EI
- Basic Platform Services
- Common Services
- Industry specific Services
Day Four:
Phyton Programing Basics Experimental Guide
• List and Tuple
• String
• Dictionary
• Conditional and Loop Statements
• Function
• Object Oriented Programming
• Date and Time
• Regular Expression
• File Manipulation
Basic Math Experimental Guide
• Linear Algebra Practices
• Probability Theory Practices
• Numerical Computation Example Practice
• Scenario
Day Five:
TensorFlow Programming Basics Experimental Guide
• Eight Knowledge Points
- Hello World
- Session
- Matrix Multiplication
- Definition of Variables
- TensorBoard Visualization
- Data Read and Processing
- Graph Operation
- Saving and Using Models
• Linear Regression — House Price Prediction
Image Recognition Programming Experimental Guide
Day Six:
• Speech Recognition Programming Experimental Guide
• Man-Machine Dialogue Programming Experimental Guide
