如何构建基于大型语言模型（LLM）的应用程序的开源课程

该课程由 Hamzafarooq 大神提供，是一个关于如何构建基于大型语言模型(LLM)的应用程序的开源课程。该课程包括了29个详细的教程，涵盖了从M架构、搜索和检索技术，以及如何构建和部署LLM应用程。此外，还包括了6个现实世界的项目案例，以及关于如何进行有效的代码编写和模型部署的互动式直播会话。

📌 Learning Outcomes

Gain a comprehensive understanding of LLM architecture

Construct and deploy real-world applications using LLMs

Learn the fundamentals of search and retrieval for AI applications

Understand encoder and decoder models at a deep level

Train, fine-tune, and deploy LLMs for enterprise use cases

Implement RAG-based architectures with open-source models

📢 Who is This Course For?

This course is not for beginners. It requires:

✅ Python programming skills

✅ Basic machine learning knowledge

It is designed for:

🔹 Machine Learning Engineers

🔹 Data Scientists

🔹 AI Researchers

🔹 Software Engineers interested in LLMs

📌 What You’ll Learn

✔ Collect and preprocess data for LLM applications

✔ Train and fine-tune pre-trained LLMs for specific tasks

✔ Evaluate model performance with appropriate metrics

✔ Deploy LLM applications via APIs and Hugging Face

✔ Address ethical concerns in AI development

📚 What’s Included?

✅ 29 in-depth lessons covering LLM architectures and RAG techniques

✅ 6 real-world projects to apply your learnings

✅ Interactive live sessions and direct instructor access

✅ Guided feedback & reflection

✅ Private community of peers

✅ Certificate upon completion

📢 Attribution & Credits

If you use my course material, content, or research in your work, please credit me and the respective contributors.

🔹 Proper citation format:

Farooq, H. (2024). Building LLM Applications from Scratch

Stanford Continuing Studies: The AI Leadership Series

📌 Tagging & mentions are always appreciated! 😊

📅 Course Syllabus

Week 1: Introduction to NLP

Understanding natural language processing fundamentals

Tokenization, embeddings, and vector representations

Week 2: Transformers & LLM System Design

The evolution of Transformer models

Understanding encoder-decoder architectures

Week 3: Semantic Search & Retrieval

Implementing vector search for LLM applications

Introduction to RAG-based architectures

Week 4: Building a Search Engine from Scratch

Developing a custom RAG solution

Optimizing search and retrieval pipelines

Week 5: The Generation Part of LLMs

Fine-tuning models for text generation tasks

Optimizing inference for real-time applications

Week 6: Prompt-Tuning, Fine-Tuning & Local LLMs

Techniques for efficient inference & quantization

Deploying custom LLMs at scale

🎉 Post-Course: Demo Day – Present your final project!

⭐ What Students Are Saying

"This course was amazing! I left feeling empowered and ready to build my own LLM-powered applications."

– Tiffany Teasley, Data Scientist

"Hamza’s approach to teaching is practical and engaging. The real-world projects made all the difference!"

– Victor Calderon, Senior ML Engineer

"One of the best courses for LLM applications! Highly recommended for anyone serious about the field."

– Abhinav, Security Researcher

🔥 Why Take This Course?

Unlike most AI courses that rely on pre-built frameworks, this course teaches you how to build LLM applications from scratch—without LangChain or LlamaIndex.

By the end, you’ll be able to:

✅ Build highly customizable LLM applications

✅ Optimize retrieval and search strategies

✅ Deploy cost-efficient and scalable AI solutions

在线网址：https://continuingstudies.stanford.edu/courses/professional-and-personal-development/the-ai-leadership-series-building-and-scaling-solutions/20243_TECH-103