Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Code and Slides
| Date | Stars |
|---|---|
| 2026-07-31 | 2388 |
| 2026-08-02 | 2387 |
| 2026-08-06 | 2387 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Building LLM Applications from Scratch 🚀 **Build LLM-Powered Applications Like a Pro!** Welcome to the Open Sourced version of my course on LLMs. This course is one of the top-rated technical courses on building **Large Language Model (LLM) applications** from the ground up. So far, I’ve taught this course to **over 1500 professionals**, at MAVEN, Stanford, UCLA and University of Minnesota, helping them gain a deep understanding of **Transformer Architecture**, **Retrieval-Augmented Generation (RAG)**, and **open-source LLM deployment**. Unlike most courses that focus on pre-built frameworks like **LangChain**, this course goes beyond by diving into the **building blocks of retrieval systems**, enabling you to **design, build, and deploy your own custom LLM-powered solutions**. 🌎 **Also featured in Stanford's AI Leadership Series:** 🔗 [Stanford AI Leadership Series - Building and Scaling AI Solutions](https://continuingstudies.stanford.edu/courses/professional-and-personal-development/the-ai-leadership-series-building-and-scaling-solutions/20243_TECH-103) --- ## 📌 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 applicati
Excerpt of 4,773 characters
Read on GitHub
36
9
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:99f52ef941f9a467, llm:Repository title 'building-llm-applications-from-scratch' and contents: 'Code and Slides' in Jupyter Notebook format — implies tutorials for building LLM applications from scratch.