Getting Started with Google Gemini AI 培训

Module 1: Introduction to AI and Google Gemini

• What is Artificial Intelligence (AI)?
• Overview of Google Gemini AI and its ecosystem
• Key features and advantages of Gemini over other AI models
• Hands-on Activity: Exploring Gemini AI through the Google AI Studio demo

Module 2: Understanding Large Language Models (LLMs)

• Fundamentals of large language models
• The architecture and operation of Gemini models
• Comparing Gemini with GPT and other leading models
• Practice Lab: Visualizing tokenization and model responses using sample prompts

Module 3: Getting Started with Gemini

• Setting up the development environment
• Working with the Gemini API and SDK
• Authentication, tokens, and API keys
• Hands-on Lab: Running your first Gemini prompt using Python

Module 4: Working with Gemini Models

• Exploring different Gemini model types and capabilities
• Selecting appropriate models for language, image, or multimodal tasks
• Initializing and testing generative models
• Practical Exercise: Comparing text-to-text and image-to-text model outputs

Module 5: Practical Applications and Use Cases

• Integrating Gemini AI into chat and Q&A applications
• Developing semantic search and summarization tools
• Ethical AI usage and bias considerations
• Group Project: Build a “Smart Research Assistant” using NotebookLM and Gemini

Module 6: Advanced Features and Customization

• Prompt optimization and advanced context handling
• Using Gemini for code generation and debugging
• Fine-tuning workflows with Google Cloud Vertex AI
• Hands-on Activity: Customizing model responses using parameters and temperature control

Module 7: Real-World Projects and Collaboration

• Collaborative project planning and workflow setup
• Integrating Gemini AI with other Google tools (Drive, Docs, Sheets)
• Team Project: Design and deploy a small AI application (e.g., content summarizer, chatbot, or idea generator)
• Peer review and discussion of project results

Module 8: Evaluation and Future Directions

• Troubleshooting common issues in Gemini projects
• Exploring the Gemini API roadmap and upcoming features
• Best practices for AI governance and scalability
• Wrap-up Activity: Reflection on practical lessons learned and career applications

Summary and Next Steps