AI Infrastructure Cloud Tpus Ko Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
本课程全面介绍AI基础设施，重点聚焦于Google Cloud TPU的使用与优化。课程共分为6个模块，总学习时间约为16-21小时，结合理论讲解、动手实验与实际案例分析，帮助学员掌握在云环境中部署和扩展AI工作负载的关键技能。适合希望深入理解高性能AI计算架构的技术人员。
Module 1: Foundations of Computing & Algorithms
Estimated time: 4 hours
- Computing best practices and industry standards
- Algorithm design for scalable systems
- Interactive lab: Building practical solutions
- Assessment through quiz and peer-reviewed assignment
Module 2: Neural Networks & Deep Learning
Estimated time: 2.5 hours
- Core concepts of neural networks
- Fundamentals of deep learning
- Hands-on exercises applying deep learning techniques
- Guided project with instructor feedback
Module 3: AI System Design & Architecture
Estimated time: 1.5 hours
- Case study analysis of real-world AI systems
- Review of common AI tools and frameworks
- Interactive lab: Building practical solutions
Module 4: Natural Language Processing
Estimated time: 3.5 hours
- Introduction to NLP key concepts
- Understanding transformer architectures and attention mechanisms
- Hands-on exercises in NLP techniques
- Guided project with instructor feedback
Module 5: Computer Vision & Pattern Recognition
Estimated time: 2 hours
- Core principles of computer vision
- Pattern recognition methods
- Case study analysis and best practices
- Assessment via quiz and peer-reviewed assignment
Module 6: Deployment & Production Systems
Estimated time: 3 hours
- Deploying AI models into production
- Hands-on exercises with deployment systems
- Case study analysis of real-world deployments
- Interactive lab: Building practical solutions
Prerequisites
- Basic understanding of cloud computing
- Familiarity with machine learning fundamentals
- Programming experience recommended
What You'll Be Able to Do After
- Design and implement scalable AI algorithms
- Apply deep learning and neural network techniques effectively
- Build and deploy AI-powered applications using cloud infrastructure
- Evaluate model performance with appropriate metrics
- Leverage Cloud TPUs for accelerating large-scale AI workloads