Generative AI for Beginners

微软官方推出的 “Generative AI for Beginners”,是专为 AI 初学者设计的免费课程,帮助零基础用户系统掌握生成式 AI 技术

收录时间:
2025-06-21

Generative AI for Beginners (Version 3) – A Course

Learn the fundamentals of building Generative AI applications with our 21-lesson comprehensive course by Microsoft Cloud Advocates.

🌱 Getting Started

This course has 21 lessons. Each lesson covers its own topic so start wherever you like!

Lessons are labeled either “Learn” lessons explaining a Generative AI concept or “Build” lessons that explain a concept and code examples in both Python and TypeScript when possible.

For .NET Developers checkout Generative AI for Beginners (.NET Edition)!

Each lesson also includes a “Keep Learning” section with additional learning tools.

What You Need

To run the code of this course, you can use either:

We have created a Course Setup lesson to help you with setting up your development environment.

Don’t forget to star (🌟) this repo to find it easier later.

🧠 Ready to Deploy?

If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both Python and TypeScript.

🗣️ Meet Other Learners, Get Support

Join our official Azure AI Foundry Discord server to meet and network with other learners taking this course and get support.

Ask questions or share product feedback in our Azure AI Foundry Developer Forum on Github.

🚀 Building a Startup?

Sign up for Microsoft for Startups Founders Hub to receive free OpenAI credits and up to $150k towards Azure credits to access OpenAI models through Azure OpenAI Services.

🙏 Want to help?

Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request

📂 Each lesson includes:

  • A short video introduction to the topic
  • A written lesson located in the README
  • Python and TypeScript code samples supporting Azure OpenAI and OpenAI API
  • Links to extra resources to continue your learning

🗃️ Lessons

# Lesson Link Description Video Extra Learning
00 Course Setup Learn: How to Setup Your Development Environment Video Coming Soon Learn More
01 Introduction to Generative AI and LLMs Learn: Understanding what Generative AI is and how Large Language Models (LLMs) work. Video Learn More
02 Exploring and comparing different LLMs Learn: How to select the right model for your use case Video Learn More
03 Using Generative AI Responsibly Learn: How to build Generative AI Applications responsibly Video Learn More
04 Understanding Prompt Engineering Fundamentals Learn: Hands-on Prompt Engineering Best Practices Video Learn More
05 Creating Advanced Prompts Learn: How to apply prompt engineering techniques that improve the outcome of your prompts. Video Learn More
06 Building Text Generation Applications Build: A text generation app using Azure OpenAI / OpenAI API Video Learn More
07 Building Chat Applications Build: Techniques for efficiently building and integrating chat applications. Video Learn More
08 Building Search Apps Vector Databases Build: A search application that uses Embeddings to search for data. Video Learn More
09 Building Image Generation Applications Build: An image generation application Video Learn More
10 Building Low Code AI Applications Build: A Generative AI application using Low Code tools Video Learn More
11 Integrating External Applications with Function Calling Build: What is function calling and its use cases for applications Video Learn More
12 Designing UX for AI Applications Learn: How to apply UX design principles when developing Generative AI Applications Video Learn More
13 Securing Your Generative AI Applications Learn: The threats and risks to AI systems and methods to secure these systems. Video Learn More
14 The Generative AI Application Lifecycle Learn: The tools and metrics to manage the LLM Lifecycle and LLMOps Video Learn More
15 Retrieval Augmented Generation (RAG) and Vector Databases Build: An application using a RAG Framework to retrieve embeddings from a Vector Databases Video Learn More
16 Open Source Models and Hugging Face Build: An application using open source models available on Hugging Face Video Learn More
17 AI Agents Build: An application using an AI Agent Framework Video Learn More
18 Fine-Tuning LLMs Learn: The what, why and how of fine-tuning LLMs Video Learn More
19 Building with SLMs Learn: The benefits of building with Small Language Models Video Coming Soon Learn More
20 Building with Mistral Models Learn: The features and differences of the Mistral Family Models Video Coming Soon Learn More
21 Building with Meta Models Learn: The features and differences of the Meta Family Models Video Coming Soon Learn More

数据统计

相关导航

大规模语言模型:从理论到实践

大规模语言模型:从理论到实践

大语言模型(Large Language Models,LLM)是一种由包含数百亿以上权重的深度神经网络构建的语言模型,使用自监督学习方法通过大量无标记文本进行训练。自2018年以来,包含Google、OpenAI、Meta、百度、华为等公司和研究机构都纷纷发布了包括BERT, GPT等在内多种模型,并在几乎所有自然语言处理任务中都表现出色。2021年开始大模型呈现爆发式的增长,特别是2022年11月ChatGPT发布后,更是引起了全世界的广泛关注。用户可以使用自然语言与系统交互,从而实现包括问答、分类、摘要、翻译、聊天等从理解到生成的各种任务。大型语言模型展现出了强大的对世界知识掌握和对语言的理解。本书将介绍大语言模型的基础理论包括语言模型、分布式模型训练以及强化学习,并以Deepspeed-Chat框架为例介绍实现大语言模型和类ChatGPT系统的实践。